SYSTEMS AND METHODS OF DETECTING DENSELY-PACKED ANALYTES

Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. These may have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy of polynucleotide sequencing applications.

BACKGROUND

Affordable, rapid sequencing is causing a revolution in medicine and healthcare globally. Whiles the price of sequencing a genome has dropped dramatically since the first human genome was sequenced in 2000, the significant milestone of sequencing a genome for $1000 was recently achieved. However, there is demand for lower cost sequencing that can enable applications such as large population sequencing, disease screening and early detection.

A standard for measuring the cost of sequencing is the price of a 30× human genome, defined as 90 Gigabases. The major cost components for sequencing systems are primarily the consumables which include biochips and reagents as well as the instrument costs.

SUMMARY

An aspect of the present disclosure comprises a method for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the method comprising: providing a substrate comprising a surface, wherein the surface comprises the plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of the plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of the optical imaging module, and wherein the surface comprises reagents for sequencing by synthesis; performing a plurality of cycles of probe binding to the plurality of analytes, a cycle of the plurality of cycles comprising: contacting the plurality of analytes with a plurality of probes, a probe of the plurality of probes comprising a detectable label; identifying the detectable labels for a cycle of the plurality of cycles, wherein the identifying comprises applying a correction based on a neighbor effect and a relative position of one or more analytes of the plurality of analytes; and identifying the analyte from the identified detectable labels across the plurality of cycles. In some embodiments, the surface is patterned. In some embodiments, the surface is unpatterned. In some embodiments, the correction comprises use of a distance-dependent correction factor. In some embodiments, the correction comprises use of a pattern-dependent correction factor. In some embodiments, the use of the pattern-dependent correction factor comprises a determination of one or more relative positions of one or more analytes of the plurality of analytes and a determination one or more distances relative to a number of pixels between the relative positions of the analytes of the plurality of analytes. In some embodiments, the one or more relative positions of the analytes and the one or more distances relative to a number of pixels between the relative positions of the analytes are applied to a reference pixel grid to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, the one or more distances relative to a number of pixels between one or more pixels adjacent to a relative position of a first analyte of the plurality of analytes and one or more pixels adjacent to a relative position of a second analyte of the plurality of analytes to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, the determination of one or more relative positions of the analytes of the plurality of analytes and the determination one or more distances relative to a number of pixels between the relative positions of the analytes of the plurality of analytes are applied to the neighboring effect of one or more adjacent analytes of the plurality of analytes to determine one or more interfering optical signals derived from the analyte, wherein the adjacent analytes are adjacent to the analytes of the plurality of analytes. In some embodiments, the relative position of the analyte of the plurality of analytes, the neighboring effect of an analyte of the plurality of analytes, or both are determined at least in part by use of a trained machine learning algorithm. In some embodiments, the analytes are DNA concatemers. In some embodiments, the DNA concatemers are hybridized to ssDNA hairs. In some embodiments, the analytes are proteins or peptides. In some embodiments, the probes comprise a plurality of reversible terminator nucleotides. In some embodiments, the plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, the resolving comprises removing interfering optical signals from a neighboring polynucleotide using a center-to-center distance between the neighboring polynucleotides from the determined relative positions. In some embodiments, the resolving function comprises deconvolution. In some embodiments, the polynucleotides are packed on the substrate such that there is overlap between optical signals emitted by the detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein the adjacent polynucleotides each comprise a distinct sequence. In some embodiments, the polynucleotides are deposited on the surface at an average density of more than 4 molecules per square micron. In some embodiments, the relative position of the analytes deposited to the surface of the substrate is determined within 10 nm RMS.

Another aspect of the present disclosure comprises a system for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the system comprising: a substrate comprising a surface, wherein the surface comprises the plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of the plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of the optical imaging module, and wherein the surface comprises reagents for sequencing by synthesis; an optical imaging device configured to perform a plurality of cycles of probe binding to the plurality of analytes, wherein one or more cycles of the plurality of cycles comprises contacting the plurality of analytes with a plurality of probes, a probe of the plurality of probes comprising a detectable label; an image processing module, the image processing module configured to: identify the detectable labels a cycle of the plurality of cycles, wherein the identifying comprises applying a correction based on a neighbor effect and a relative position of one or more analytes of the plurality of analytes; and identify the analytes disposed on the surface of the substrate from the identified detectable labels across the plurality of cycles. In some embodiments, the surface is patterned. In some embodiments, the surface is unpatterned. In some embodiments, the correction comprises use of a distance-dependent correction factor. In some embodiments, the correction comprises use of a pattern-dependent correction factor. In some embodiments, the use of the pattern-dependent correction factor comprises a determination of one or more relative positions of the analytes of the plurality of analytes and a determination one or more distances relative to a number of pixels between the relative positions of the analytes of the plurality of analytes. In some embodiments, the one or more relative positions of the analytes and the one or more distances relative to a number of pixels between the relative positions of the analytes are applied to a reference pixel grid to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, the one or more distances relative to a number of pixels between one or more pixels adjacent to a relative position of a first analyte of the plurality of analytes and one or more pixels adjacent to a relative position of a second analyte of the plurality of analytes to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, the determination of one or more relative positions of the analytes of the plurality of analytes and the determination one or more distances relative to a number of pixels between the relative positions of the analytes of the plurality of analytes are applied to the neighboring effect of an analyte of the plurality of analytes to determine one or more interfering optical signals derived from the analyte. In some embodiments, the analytes are DNA concatemers. In some embodiments, the DNA concatemers are hybridized to ssDNA hairs. In some embodiments, the analytes are proteins or peptides. In some embodiments, the probes comprise a plurality of reversible terminator nucleotides. In some embodiments, the plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, the resolving comprises removing interfering optical signals from a neighboring polynucleotide using a center-to-center distance between the neighboring polynucleotides from the determined relative positions. In some embodiments, the resolving function comprises deconvolution. In some embodiments, the polynucleotides are packed on the substrate such that there is overlap between optical signals emitted by the detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein the adjacent polynucleotides each comprise a distinct sequence. In some embodiments, the polynucleotides are deposited on the surface at an average density of more than 4 molecules per square micron. In some embodiments, the relative position of the analytes deposited to the surface of the substrate is determined within 10 nm RMS.

Another aspect of the present disclosure comprises a method for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the method comprising: providing a substrate comprising a surface, wherein the surface comprises the plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of the plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of the optical imaging module, and wherein the surface further comprises reagents for sequencing by synthesis; and performing a plurality of cycles of probe binding to the plurality of analytes wherein a cycle of the plurality of cycles comprises contacting the plurality of analytes with a plurality of probes, a probe of the plurality of probes comprising a detectable label; and cleaving one or more detectable labels by applying a cleaving solution. In some embodiments, the surface is patterned. In some embodiments, the surface is unpatterned. In some embodiments, the analytes are DNA concatemers. In some embodiments, the DNA concatemers are hybridized to ssDNA hairs. In some embodiments, the analytes are proteins or peptides. In some embodiments, the probes comprise a plurality of reversible terminator nucleotides. In some embodiments, the plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, the cleaving solution comprises TCEP ((tris(2-carboxyethyl)phosphine) and THPP (Tris(hydroxypropyl)phosphine). In some embodiments, the TCEP has a concentration of about 10 mM to about 150 mM. In some embodiments, the TCEP has a concentration of about 150 mM. In some embodiments, the THPP has a concentration of about 5 mM to about 100 mM. In some [preferred?] embodiments the TCEP has a concentration of 150 mM and the THPP has a concentration of 40 or 50 mM.

Another aspect of the present disclosure comprises a system for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the system comprising: a substrate comprising a surface, wherein the surface comprises the plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of the plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of the optical imaging module, and wherein the surface comprises reagents for sequencing by synthesis; an optical imaging device configured to perform a plurality of cycles of probe binding to the plurality of analytes, wherein one or more cycles of the plurality of cycles comprises contacting the plurality of analytes with a plurality of probes, a probe of the plurality of probes comprising a detectable label; and a dispenser dispensing a cleaving solution to cleave the detectable label from the analyte.

In some embodiments, the surface is patterned. In some embodiments, the surface is unpatterned. In some embodiments, the analytes are DNA concatemers. In some embodiments, the DNA concatemers are hybridized to ssDNA hairs. In some embodiments, the analytes are proteins or peptides. In some embodiments, the probes comprise a plurality of reversible terminator nucleotides. In some embodiments, the plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, the polynucleotides are packed on the substrate such that there is overlap between optical signals emitted by the detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein the adjacent polynucleotides each comprise a distinct sequence. In some embodiments, the polynucleotides are deposited on the surface at an average density of more than 4 molecules per square micron. In some embodiments, the cleaving solution comprises TCEP ((tris(2-carboxyethyl)phosphine) and THPP (Tris(hydroxypropyl)phosphine). In some embodiments, the TCEP has a concentration of about 10 mM to about 150 mM. In some embodiments, the TCEP has a concentration of about 150 mM. In some embodiments, the THPP has a concentration of about 5 mM to about 100 mM. In some [preferred?] embodiments the TCEP has a concentration of 150 mM and the THPP has a concentration of 40 or 50 mM.

An aspect of the present disclosure comprises a method for sequencing a plurality of analytes disposed at high density on a surface of a substrate, comprising: providing a substrate comprising a surface, wherein the surface comprises a plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module, and wherein said surface comprises reagents for sequencing by synthesis; performing a plurality of cycles of probe binding to said plurality of analytes, a cycle of said plurality of cycles comprising: contacting said plurality of analytes with a plurality of probes, a probe of said plurality of probes comprising a detectable label; (ii) imaging a field of said surface with an optical system to detect an optical signal from each probe brought in contact with said plurality of analytes, thereby detecting a plurality of optical signals in said field for said cycle; determining a peak location from each of said plurality of optical signals from images of said field from at least two of said plurality of cycles; overlaying said peak locations for each optical signal and applying an optical distribution model at each cluster of optical signals to determine a relative position of each detected probe on said surface; resolving said optical signals in each field image from each cycle using said determined relative position and a resolving function; identifying said detectable labels for each field and each cycle from said deconvolved optical signals; and identifying analytes disposed on the surface of the substrate from said identified detectable labels across said plurality of cycles at each analyte position. In some embodiments, concatemers are loaded on the surface and closely packed to enable a center to center distance of −250 nanometers (nm) with a variance of +/−25 nm. In some embodiments, the average center-to-center distance between molecules of about 315 nm. In some embodiments, the plurality of analytes (e.g., nucleic acid molecules) may be deposited adjacent to a surface such that adjacent analytes of the plurality of analytes may have average center-to-center spacings of at least 10 nanometers (nm), 50 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, or more. The average center-to-center spacings may be less than or equal to 500 nm, 490 nm, 480 nm, 470 nm, 460 nm, 450 nm, 440 nm, 430 nm, 420 nm, 410 nm, 400 nm, 390 nm, 380 nm, 370 nm, 360 nm, 350 nm, 340 nm, 330 nm, 320 nm, 310 nm, 300 nm, 290 nm, 280 nm, 270 nm, 260 nm, 250 nm, 240 nm, 230 nm, 220 nm, 210 nm, 200 nm, 190 nm, 180 nm, 170 nm, 160 nm, 150 nm, 140 nm, 130 nm, 120 nm, 110 nm, 100 nm, 50 nm, or less. The plurality of analytes may be nucleic acid molecules (DNA and/or RNA), proteins and/or polypeptides. The plurality of analytes may be disposed adjacent to a surface such that an individual analyte of the plurality of analytes may be resolved (e.g., optically resolved). The plurality of analytes may be disposed adjacent to the surface such that adjacent analytes of the plurality of analytes do not touch or contact each other. In some embodiments, said surface is unpatterned. In some embodiments, said surface is patterned. In some embodiments, one or more analytes of said plurality of analytes are treated with a repellant or attractive substance. In some embodiments, said repellant or attractive substance comprises zwitterionic features. In some embodiments, said repellant or attractive substance comprises PEG, a polysaccharide, ampholine ampholytes, sulphobetaine, and/or BSA. In some embodiments, said analytes are DNA concatemers. In some embodiments, said DNA concatemers are hybridized to ssDNA hairs. In some embodiments, said analytes are proteins or peptides. In some embodiments, said probes comprise a plurality of reversible terminator nucleotides. In some embodiments, said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, said resolving comprises removing interfering optical signals from neighboring polynucleotides using a center-to-center distance between said neighboring polynucleotides from said determined relative positions. In some embodiments, said resolving function comprises machine learning. In some embodiments, said resolving function comprises nearest neighbor variable regression. In some embodiments, said polynucleotides are packed on said substrate such that there is overlap between optical signals emitted by said detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein said adjacent polynucleotides each comprise a distinct sequence. In some embodiments, the polynucleotides are deposited on said surface at an average density of more than 4 molecules per square micron. In some embodiments, said imaging of said surface is performed at a resolution of one pixel per 300 nm or higher along an axis of the image field. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 250 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 200 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 150 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 100 nanometers or higher. In some embodiments, the method further comprises generating an oversampled image with a higher pixel density from each of said field images from each cycle. In some embodiments, said overlaying said peak locations comprises aligning positions of said optical signal peaks detected in each field for a plurality of said cycles to generate a cluster of optical peak positions for each polynucleotide from said plurality of cycles. In some embodiments, said overlaying said peak locations comprises aligning positions of said optical signal peaks detected in each field for a subset of said cycles to generate a cluster of optical peak positions for each polynucleotide from said subset of cycles. In some embodiments, said optical distribution model comprises a point spread function. In some embodiments, said relative position of said analytes deposited to the surface of the substrate is determined within 10 nm RMS.

Another aspect of the present disclosure comprises a method for accurately determining a relative position of analytes deposited on a surface of a packed substrate, comprising: providing a substrate comprising a surface, wherein the surface comprises a plurality of analytes deposited on the surface at discrete locations; performing a plurality of cycles of probe binding and signal detection on said surface, each cycle comprising: contacting said analytes with a plurality of probes from a probe set, wherein said probes comprise a detectable label, wherein each of said probes binds specifically to a target analyte; and imaging a field of said surface with an optical system to detect a plurality of optical signals from individual probes bound to said analytes at discrete locations on said surface; determining a peak location from each of said plurality of optical signals from images of said field from at least two of said plurality of cycles; and overlaying said peak locations for each optical signal and applying an optical distribution model at each cluster of optical signals to determine a relative position of each detected analyte on said surface with improved accuracy. In some embodiments, concatemers are loaded on the surface and closely packed to enable a center to center distance of −250 nm with a variance of +/−25 nm. In some embodiments, the average center-to-center distance between molecules of about 315 nm. In some embodiments, the plurality of analytes (e.g., nucleic acid molecules) may be deposited adjacent to a surface such that adjacent analytes of the plurality of analytes may have average center-to-center spacings of at least 10 nanometers (nm), 50 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, or more. The average center-to-center spacings may be less than or equal to 500 nm, 490 nm, 480 nm, 470 nm, 460 nm, 450 nm, 440 nm, 430 nm, 420 nm, 410 nm, 400 nm, 390 nm, 380 nm, 370 nm, 360 nm, 350 nm, 340 nm, 330 nm, 320 nm, 310 nm, 300 nm, 290 nm, 280 nm, 270 nm, 260 nm, 250 nm, 240 nm, 230 nm, 220 nm, 210 nm, 200 nm, 190 nm, 180 nm, 170 nm, 160 nm, 150 nm, 140 nm, 130 nm, 120 nm, 110 nm, 100 nm, 50 nm, or less. In some embodiments, said surface is unpatterned. In some embodiments, said surface is patterned. In some embodiments, the method further comprises: resolving said optical signals in each field image from each cycle using said determined relative position and a resolving function; and identifying said detectable labels bound to said deposited analytes for each field and each cycle from said deconvolved optical signals. In some embodiments, one or more analytes of said plurality of analytes are treated with a repellant or attractive substance. In some embodiments, said repellant or attractive substance comprises zwitterionic features. In some embodiments, said repellant or attractive substance comprises PEG, a polysaccharide, ampholine ampholytes, sulphobetaine, and/or BSA. In some embodiments, said analytes are DNA concatemers. In some embodiments, said DNA concatemers are hybridized to ssDNA hairs. In some embodiments, said analytes are proteins or peptides. In some embodiments, the method further comprises using said detectable label identity for each analyte detected at each cycle to identify a plurality of said analytes on said substrate. In some embodiments, said resolving comprises removing interfering optical signals from neighboring analytes using a center-to-center distance between said neighboring analytes from said determined relative positions of said neighboring analytes. In some embodiments, said resolving function comprises machine learning. In some embodiments, said resolving function comprises nearest neighbor variable regression. In some embodiments, said analytes are single biomolecules. In some embodiments, said analytes deposited on said surface are spaced apart on average less than the diffraction limit of the light emitted by the detectable labels and imaged by the optical system. In some embodiments, the deposited analytes comprises an average center-to-center distance between each analyte and the nearest adjacent analyte of less than 500 nm. In some embodiments, said overlaying said peak locations comprises aligning positions of said optical signal peaks detected in each field for a plurality of said cycles to generate a cluster of optical peak positions for each analyte from said plurality of cycles. In some embodiments, said relative position is determined with an accuracy of within 10 nm RMS. In some embodiments, said method resolves optical signals from a surface at a density of about 4 to about 25 analytes per square micron.

Another aspect of the present disclosure comprises a system for determining the identity of a plurality of analytes, comprising an optical imaging device configured to image a plurality of optical signals from a field of a substrate over a plurality of cycles of probe binding to analytes deposited on a surface of the substrate; and an image processing module, said module configured to: determine a peak location from each of said plurality of optical signals from images of said field from at least two of said plurality of cycles; determine a relative position of each detected analyte on said surface with improved accuracy by applying an optical distribution model to each cluster of optical signals from said plurality of cycles; and deconvolve said optical signals in each field image from each cycle using said determined relative position and a resolving function. In some embodiments, concatemers are loaded on the surface and closely packed to enable a center to center distance of −250 nm with a variance of +/−25 nm. In some embodiments, the average center-to-center distance between molecules of about 315 nm. In some embodiments, the plurality of analytes (e.g., nucleic acid molecules) may be deposited adjacent to a surface such that adjacent analytes of the plurality of analytes may have average center-to-center spacings of at least 10 nanometers (nm), 50 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, or more. The average center-to-center spacings may be less than or equal to 500 nm, 490 nm, 480 nm, 470 nm, 460 nm, 450 nm, 440 nm, 430 nm, 420 nm, 410 nm, 400 nm, 390 nm, 380 nm, 370 nm, 360 nm, 350 nm, 340 nm, 330 nm, 320 nm, 310 nm, 300 nm, 290 nm, 280 nm, 270 nm, 260 nm, 250 nm, 240 nm, 230 nm, 220 nm, 210 nm, 200 nm, 190 nm, 180 nm, 170 nm, 160 nm, 150 nm, 140 nm, 130 nm, 120 nm, 110 nm, 100 nm, 50 nm, or less. In some embodiments, said surface is patterned. In some embodiments, said surface is unpatterned. In some embodiments, said surface is patterned. In some embodiments, said image processing module is further configured to determine an identity of said analytes deposited on said surface using said deconvolved optical signals. In some embodiments, said optical image device comprises a moveable stage defining a scannable area. In some embodiments, said optical image device comprises a sensor and optical magnification configured to sample a surface of a substrate at below the diffraction limit in said scannable area. In some embodiments, the system further comprises a substrate comprising analytes deposited to a surface of the substrate at a center-to-center spacing below the diffraction limit. In some embodiments, said resolving comprises removing interfering optical signals from neighboring analytes using a center-to-center distance between said neighboring analytes to determine said relative positions of said neighboring analytes. In some embodiments, said surface is unpatterned. In some embodiments, said surface is patterned.

Another aspect of the present disclosure comprises a method for processing or analyzing a plurality of analytes, comprising: disposing said plurality of analytes adjacent to a surface of a substrate at a density wherein a minimum effective pitch is less than a measure of λ/(2*NA); obtaining a plurality of optical signals from said substrate over one or more cycles of probes binding to analytes of said plurality of analytes disposed adjacent to said substrate, wherein at least a subset of said plurality of optical signals overlap, which plurality of optical signals comprise light having a wavelength (λ); applying an imaging algorithm to process said plurality of optical signals to identify a position of an analyte of said plurality of analytes or a relative position of said analyte with respect to another analyte of said plurality of analytes; and using said positions or relative positions to identify said analytes of said plurality of analytes. In some embodiments, concatemers are loaded on the surface and closely packed to enable a center to center distance of −250 nm with a variance of +/−25 nm. In some embodiments, the average center-to-center distance between molecules of about 315 nm. In some embodiments, the plurality of analytes (e.g., nucleic acid molecules) may be deposited adjacent to a surface such that adjacent analytes of the plurality of analytes may have average center-to-center spacings of at least 10 nanometers (nm), 50 nm, 100 nm, 110 nm, 120 nm, 130 nm, 140 nm, 150 nm, 160 nm, 170 nm, 180 nm, 190 nm, 200 nm, 210 nm, 220 nm, 230 nm, 240 nm, 250 nm, 260 nm, 270 nm, 280 nm, 290 nm, 300 nm, 310 nm, 320 nm, 330 nm, 340 nm, 350 nm, 360 nm, 370 nm, 380 nm, 390 nm, 400 nm, 410 nm, 420 nm, 430 nm, 440 nm, 450 nm, 460 nm, 470 nm, 480 nm, 490 nm, 500 nm, or more. The average center-to-center spacings may be less than or equal to 500 nm, 490 nm, 480 nm, 470 nm, 460 nm, 450 nm, 440 nm, 430 nm, 420 nm, 410 nm, 400 nm, 390 nm, 380 nm, 370 nm, 360 nm, 350 nm, 340 nm, 330 nm, 320 nm, 310 nm, 300 nm, 290 nm, 280 nm, 270 nm, 260 nm, 250 nm, 240 nm, 230 nm, 220 nm, 210 nm, 200 nm, 190 nm, 180 nm, 170 nm, 160 nm, 150 nm, 140 nm, 130 nm, 120 nm, 110 nm, 100 nm, 50 nm, or less. In some embodiments, said surface is unpatterned. In some embodiments, said surface is patterned. In some embodiments, one or more analytes of said plurality of analytes are treated with a repellant or attractive substance. In some embodiments, said repellant or attractive substance comprises zwitterionic features. In some embodiments, said repellant or attractive substance comprises PEG, a polysaccharide, ampholine ampholytes, sulphobetaine, and/or BSA. In some embodiments, said analytes are DNA concatemers. In some embodiments, said DNA concatemers are hybridized to ssDNA hairs. In some embodiments, said analytes are proteins or peptides. In some embodiments, operation (b) further comprises configuring an optical processing module to overlay said plurality of optical signals from said one or more cycles of probes binding to analytes and operation (c) further comprises applying an optical distribution model said overlay of said plurality of optical signals to determine a relative position of each detected analyte. In some embodiments, said imaging algorithm comprises a resolving function. In some embodiments, said resolving function comprises machine learning. In some embodiments, said resolving function comprises nearest neighbor variable regression. In some embodiments, said resolving function comprises removing interfering optical signals from neighboring analytes using a center-to-center distance between said neighboring analytes. In some embodiments, said plurality of analytes are disposed adjacent to said substrate at a density of about 1 to 25 molecules per square micron. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 300 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 250 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 200 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 150 nanometers or higher. In some embodiments, an optical imagining module is configured to obtain said plurality of optical signals at a resolution of one pixel per 100 nanometers or higher.

INCORPORATION BY REFERENCE

DETAILED DESCRIPTION

Whenever the term “erythorbic acid” is used herein, the term refers to erythorbic acid, isoascorbic acid, its isomers (e.g. the L-isomer and D-isomer), salts thereof, analogues thereof, derivatives thereof, or any mixtures thereof, including racemic mixtures.

As used herein, the term “center-to-center distance” generally refers to a distance between two adjacent molecules as measured by the difference between the average position of each molecule on a substrate. The term “average minimum center-to-center distance” refers specifically to the average distance between the center of each analyte disposed on the substrate and the center of its nearest neighboring analyte, although the term “center-to-center distance” refers also to the minimum center-to-center distance in the context of limitations corresponding to the density of analytes on the substrate.

As used herein, the term “pitch” or “average effective pitch” is generally used to refer to average minimum center-to-center distance. In the context of regular arrays of analytes, pitch may also be used to determine a center-to-center distance between adjacent molecules along a defined axis.

As used herein, the term “overlaying” (e.g., overlaying images) generally refers to overlaying images from different cycles to generate a distribution of detected optical signals (e.g., position and intensity, or position of peak) from each analyte over a plurality of cycles. This distribution of detected optical signals can be generated by overlaying images, overlaying artificial processed images, or overlaying datasets comprising positional information. Thus, as used herein, the term “overlaying images” generally encompasses any of these mechanisms to generate a distribution of position information for optical signals from a single probe bound to a single analyte for each of a plurality of cycles.

A “cycle” is generally defined by completion of one or more passes and stripping of the detectable label from the substrate. Subsequent cycles of one or more passes per cycle can be performed. For the methods and systems described herein, multiple cycles are performed on a single substrate or sample. For deoxyribonucleic acid (DNA) sequencing, multiple cycles may require the use of a reversible terminator and a removable detectable label from an incorporated nucleotide. For proteins, multiple cycles may require that the probe removal (stripping) conditions either maintain proteins folded in their proper configuration, or that the probes used are chosen to bind to peptide sequences so that the binding efficiency is independent of the protein fold configuration.

A “pass” in a detection assay generally refers to a process where a plurality of probes comprising a detectable label are introduced to the bound analytes, selective binding occurs between the probes and distinct target analytes, and a plurality of signals are detected from the detectable labels. A pass includes introduction of a set of antibodies that bind specifically to a target analyte. A pass can also include introduction of a set of labelled nucleotides for incorporation into the growing strand during sequencing by synthesis. There can be multiple passes of different sets of probes before the substrate is stripped of all detectable labels, or before the detectable label or reversible terminator is removed from an incorporated nucleotide during sequencing. In general, if four nucleotides are used during a pass, a cycle may only include a single pass for standard four nucleotide sequencing by synthesis.

As used herein, an “image” generally refers to an image of a field taken during a cycle or a pass within a cycle. In some embodiments, a single image is limited to detection of a single color of a detectable label.

As used herein, the term “field” generally refers to a single region of a substrate that is imaged. During a typical assay a single field is imaged at least once per cycle. For example, for a 20-cycle assay, with 4 colors, there can be 20*4=80 images, all of the same field.

A “target analyte” or “analyte” generally refers to a molecule, compound, complex, substance or component that is to be identified, quantified, and otherwise characterized. A target analyte can comprise by way of example, but not limitation to, a single molecule (of any molecular size), a single biomolecule, a polypeptide, a protein (folded or unfolded), a polynucleotide molecule (ribonucleic acid (RNA), complementary DNA (cDNA), or DNA), a fragment thereof, a modified molecule thereof, such as a modified nucleic acid, or a combination thereof. In an embodiment, a target polynucleotide comprises a hybridized primer to facilitate sequencing by synthesis. The target analytes are recognized by probes, which can be used to sequence, identify, and quantify the target analytes using optical detection methods described herein.

A “probe,” as used herein generally refers to a molecule that is capable of binding to other molecules (e.g., a complementary labelled nucleotide during sequencing by synthesis, polynucleotides, polypeptides or full-length proteins, etc.), cellular components or structures (lipids, cell walls, etc.), or cells for detecting or assessing the properties of the molecules, cellular components or structures, or cells. The probe comprises a structure or component that binds to the target analyte. In some embodiments, multiple probes may recognize different parts of the same target analyte. Examples of probes include, but are not limited to, a labelled reversible terminator nucleotide, an aptamer, an antibody, a polypeptide, an oligonucleotide (DNA, RNA), or any combination thereof. Antibodies, aptamers, oligonucleotide sequences and combinations thereof as probes are also described in detail below.

The probe can comprise a detectable label that is used to detect the binding of the probe to a target analyte. The probe can be directly or indirectly bound to, hybridized to, conjugated to, or covalently linked to the target analyte.

As used herein, the term “detectable label” generally refers to a molecule bound to a probe that can generate a detectable optical signal when the probe is bound to a target analyte and imaged using an optical imaging system. The detectable label can be directly or indirectly bound to, hybridized to, conjugated to, or covalently linked to the probe. In some embodiments, the detectable label is a fluorescent molecule or a chemiluminescent molecule. The probe can be detected optically via the detectable label. In some embodiments, the detectable label is detected when the detectable label is exposed to excitation energy.

As used herein, the term “optical distribution model” generally refers to a statistical distribution of probabilities for light detection from a point source. These include, for example, a Gaussian distribution. The Gaussian distribution can be modified to include anticipated aberrations in detection to generate a point spread function as an optical distribution model.

Provided herein are systems and methods that facilitate optical detection and discrimination of probes bound to tightly packed analytes bound to the surface of a substrate. In part, the methods and systems described herein rely on repeated detection of a plurality of target analytes on the surface of a substrate to improve the accuracy of identification of a relative location of each analyte on the substrate. This information can then be used to perform signal resolving on each image of a field of the substrate for each cycle to reliably identify a signal from a probe bound to the target analyte. In some embodiments, the resolving comprises deconvolution. In some embodiments, this type of deconvolution processing can be used to distinguish between different probes bound to the target analyte that have overlapping emission spectrum when activated by an activating light. In some embodiments, the deconvolution processing can be used to separate optical signals from neighboring analytes. This is especially useful for substrates with analytes having a density wherein optical detection is challenging due to the diffraction limit of optical systems.

In some embodiments, the methods and systems described herein are useful in sequencing. By providing methods and systems that facilitate reliable optical detection on packed substrates, costs associated with sequencing, such as reagents, number of clonal molecules used, processing and read time, can all be reduced to advance sequencing technologies, specifically, sequencing by synthesis using optically detected nucleotides.

Although the systems and methods described herein may have important implications for advancing sequencing technology, the methods and systems described herein are generally applicable to optical detection of analytes bound to the surface of a substrate, including on the single molecule level.

Sequencing Cost Reduction

Sequencing technologies include image-based systems developed by companies such as Illumina and Complete Genomics and electrical based systems developed by companies such as Ion Torrent and Oxford Nanopore. Image-based sequencing systems currently have the lowest sequencing costs of existing sequencing technologies. Image-based systems achieve low cost through the combination of high throughput imaging optics and low-cost consumables. However, prior art optical detection systems have minimum center-to-center spacing between adjacent resolvable molecules of about a micron, in part due to the diffraction limit of optical systems. In some embodiments, described herein are methods for attaining significantly lower costs for an image-based sequencing system using existing biochemistries using cycled detection, determination of precise positions of analytes, and use of the positional information for highly accurate deconvolution of imaged signals to accommodate increased packing densities below the diffraction limit.

Densely-Packed Analyte Layers and Detection Methods

Provided herein are systems and methods to facilitate imaging of signals from analytes deposited on a surface with a center-to-center spacing below the diffraction limit. These systems and methods may use advanced imaging systems to generate super-resolution images, and cycled detection to facilitate positional determination of molecules on the substrate with high accuracy and resolving of images to obtain signal identity for each molecule on a packed surface with high accuracy. These methods and systems may allow sequencing by synthesis on a packed substrate to provide highly efficient and very high throughput polynucleotide sequence determination with high accuracy.

The major cost components for sequencing systems may be primarily the consumables which include biochip and reagents and secondarily the instrument costs. To reach a $10 30× genome, a 100-fold cost reduction, the amount of data per unit area needs to increase by 100-fold and the amount of reagent per data point needs to drop by 100-fold.

FIG.1shows sequencer throughput versus array pitch and outlines a system design which meets the criteria needed for a $10 genome. The basic idea is that to achieve a 100-fold cost reduction, the amount of data per unit area needs to increase by 100-fold and the amount of reagent per data point needs to drop by 100-fold. To achieve these reductions in costs, provided herein are methods and systems that may facilitate reliable sequencing of polynucleotides deposited on the surface of a substrate at a density below the diffraction limit. These high densities may allow for more efficient usage of reagents and increase the amount of data per unit area. In addition, the increase in the reliability of detection may allow for a decrease in the number of clonal copies that may be synthesized to identify and correct errors in sequencing and detection, further reducing reagent costs and data processing costs.

High Density Distributions of Analytes on a Surface of a Substrate

FIG.2Ashows a proposed embodiment of a high-density region of 80 nm diameter binding regions (spots) on a 240 nm pitch. In this embodiment, an ordered array can be used where single-stranded DNA molecule exclusively binds to specified regions on chip. In some embodiments, concatemers (e.g., a long continuous DNA molecule that contains multiple copies of the same DNA sequence linked in series) smaller than 40 kB are used so as to not overfill the spot. The size of the concatemers may scale roughly with area, meaning the projected length of the smaller concatemer may be approximate 4 kB to 5 kB resulting in approximately 10 copies if the same amplification process is used. It is also possible to use 4 kB lengths of DNA and sequence each concatemer directly. Another option may be to bind a shorter segment of DNA with unsequenced filler DNA to bring the total length up to the size needed to create an exclusionary molecule.

FIG.2Bis a comparison of the proposed pitch compared to a sample effective pitch used for a $1,000 genome. The density of the new array is 170-fold higher, meeting the criteria of achieving 100-fold higher density. The number of copies per imaging spot per unit area also meets the criteria of being at least 100-fold lower than the prior existing platform. This may enable reagent costs 100-fold more cost effective than baseline.

Imaging Densely Packed Single Biomolecules and the Diffraction Limit

One constraint for increased molecular density for an imaging platform may be the diffraction limit. The equation for the diffraction limit of an optical system is: D=λ/12NA where D is the diffraction limit, λ, is the wavelength of light, and NA is the numerical aperture of the optical system. Typical air imaging systems have NA's of 1.0 to 1.2. Using λ, =600 nm, the diffraction limit is between 250 nm and 300 nm. For a water immersion system, the NA is −1.0, giving a diffraction limit of 300 nm.

If features on an array or other substrate surface comprising biomolecules are too close, two optical signals may overlap substantially such that a single feature that cannot be reliably resolved based on the image alone may be present. This can be exacerbated by errors introduced by the optical imaging system, such as blur due to inaccurate tracking of a moving substrate, or optical variations in the light path between the sensor and the surface of a substrate.

The transmitted light or fluorescence emission wavefronts emanating from a point in the specimen plane of the microscope may become diffracted at the edges of the objective aperture, effectively spreading the wavefronts to produce an image of the point source that is broadened into a diffraction pattern having a central disk of finite, but larger size than the original point. Therefore, due to diffraction of light, the image of a specimen may never perfectly represent the real details present in the specimen because there is a lower limit below which the microscope optical system cannot resolve structural details.

The observation of sub-wavelength structures with microscopes is difficult because of the diffraction limit. A point object in a microscope, such as a fluorescent protein or polynucleotide, may generate an image at the intermediate plane that may include a diffraction pattern created by the action of interference. When highly magnified, the diffraction pattern of the point object may be observed to include a central spot (diffraction disk) surrounded by a series of diffraction rings. Combined, this point source diffraction pattern is referred to as an Airy disk.

The size of the central spot in the Airy pattern is related to the wavelength of light and the aperture angle of the objective. For a microscope objective, the aperture angle may be described by the numerical aperture (NA), which includes the term sin (0), the half angle over which the objective can gather light from the specimen. In terms of resolution, the radius of the diffraction Airy disk in the lateral (x,y) image plane is defined by the following formula: Abbe Resolution=λ/2*NA, where λ is the average wavelength of illumination in transmitted light or the excitation wavelength band in fluorescence. The objective numerical aperture (NA=n·sin(θ)) is defined by the refractive index of the imaging medium (n; usually air, water, glycerin, or oil) multiplied by the sine of the aperture angle (sin(θ)). As a result of this relationship, the size of the spot created by a point source decreases with decreasing wavelength and increasing numerical aperture, but always remains a disk of finite diameter. The Abbe resolution (e.g., Abbe limit) is also referred to herein as the diffraction limit and defines the resolution limit of the optical system.

If the distance between the two Airy disks or point-spread functions is greater than the diffraction limit, the two-point sources are considered to be resolved (and can readily be distinguished). Otherwise, the Airy disks merge together and are considered not to be resolved.

Thus, light emitted from a detectable label point source with wavelength 2, traveling in a medium with refractive index n and converging to a spot with half-angle θ may make a diffraction limited spot with a diameter: d=212*NA. Considering green light around 500 nm and a NA (Numerical Aperture) of 1, the diffraction limit is roughly d=λ/2=250 nm (0.25 pm), which limits the density of analytes such as proteins, nucleotides and other sequencing substrates (e.g., as shown inFIG.20) on a surface able to be imaged by conventional imaging techniques. As used herein, sequencing substrates include any analyte that sequence information can be derived from, such as a template for a sequencing reaction. Even in cases where an optical microscope is equipped with the highest available quality of lens elements, is perfectly aligned, and has the highest numerical aperture, the resolution may remain limited to approximately half the wavelength of light in the best-case scenario. To increase the resolution, shorter wavelengths can be used such as UV and X-ray microscopes. These techniques offer better resolution but are expensive, suffer from lack of contrast in biological samples and may damage the sample.

Image Resolving

In some embodiments, the image resolving methods described herein comprise deconvolution. Deconvolution is an algorithm-based process used to reverse the effects of convolution on recorded data. The concept of deconvolution is widely used in the techniques of signal processing and image processing. Because these techniques are in turn widely used in many scientific and engineering disciplines, deconvolution finds many applications.

In optics and imaging, the term “deconvolution” may refer to the process of reversing the optical distortion that takes place in an optical microscope, electron microscope, telescope, or other imaging instrument, thus creating clearer images. It may be performed in the digital domain by a software algorithm, as part of a suite of microscope image processing techniques.

One method may be to assume that the optical path through the instrument is optically perfect, convolved with a point spread function (PSF), that is, a mathematical function that describes the distortion in terms of the pathway a theoretical point source of light (or other waves) takes through the instrument. Usually, such a point source contributes a small area of fuzziness to the image. If this function can be determined, it is then a matter of computing its inverse or complementary function and convolving the acquired image with that. Deconvolution may map to division in the Fourier co-domain. This allows deconvolution to be easily applied with experimental data that are subject to a Fourier transform. An example is NMR spectroscopy where the data may be recorded in the time domain, but analyzed in the frequency domain. Division of the time-domain data by an exponential function has the effect of reducing the width of Lorenzian lines in the frequency domain. The result is the original, undistorted image.

However, for diffraction limited imaging, deconvolution may also be needed to further refine the signals to improve resolution beyond the diffraction limit, even if the point spread function is perfectly known. It may be difficult to separate two objects reliably at distances smaller than the Nyquist distance. However, described herein are methods and systems using cycled detection, analyte position determination, alignment, and deconvolution which may reliably detect objects separated by distances smaller than the Nyquist distance.

Making High Density Random Layers of Concatemers for Sequencing

Also provided herein are methods of making and using high density concatemer layers. In some embodiments, the concatemers are randomly distributed on a surface of a substrate in a close-packed layer for individual detection and sequencing. In some embodiments, provided herein are methods of making and randomly distributing a layer of concatemers on a substrate such that they achieve a high density or average center-to-center distance.

Concatemers (e.g., CATs), are long single-stranded DNA molecules made through rolling circle amplification (RCA) of a ssCircular DNA. In some embodiments, the concatemers each comprise from a few up to several hundred copies of a target DNA sequence inserted between known sequence adapters. A library of concatemers comprising target DNA sequences can be generated. In some embodiments, the concatemers comprise features that self-exclude to facilitate layering a close-packed single layer of concatemers on a substrate with minimal overlap or a minimum distance between adjacent concatemers and without needing specific attachment points on the substrate. These exclusionary features facilitate close-packed layers while minimizing the number of nearest neighbor concatemers that are too close to be resolved by optical imaging, as described herein.

In some embodiments, provided herein are substrates comprising a surface, wherein the surface is bound to a close-packed, randomly distributed collection of amplified targets, such as DNA concatemers.

In some embodiments, this substrate is used to facilitate nucleotide sequencing, including of whole genomes or exomes. In some embodiments, large numbers of individual cellular targets can be sequenced. These can represent a selected panel of targets using cluster sequencing. Sequencing as described herein can be used, for example, to (i) detect multiple genetic variants (e.g., for genotyping, drug resistance determination, paternity, or identification), (ii) sequence multiple cDNA molecules for gene expression analysis for enumeration of pathway dynamics, or (iii) detect methylated residues on a target polynucleotide following bisulfate treatment. In some embodiments, sequencing methods require target amplification to generate small clusters of −200 target copies as described in the embodiments.

The method, in one embodiment, comprises: the creation of circularized single stranded molecules for targets across the genome using ligase reactions, amplification of the circularized DNA using isothermal whole genome amplification methods to generate clusters of circularized amplified targets (CAT) that have a few hundred copies, and ensuring that the CATs are coated with appropriate reagents to generate nanospheres that have a uniform size around 250 nm with a distribution around 225-275 nm.

The method, in one embodiment further comprises: distributing the CATs on a bio-chip in a densely packed collection, attaching them to the surface with removal of the coating materials, and ensuring that the CATs remain bound to the slide through multiple cycles of sequencing reactions.

In some embodiments, the target biomolecules are detected and/or sequenced and authenticated based on repeat hybridizations. This may facilitate improved accuracy, including a decrease in sensitivity and/or specificity to provide improved target identification and/or sequencing.

In some embodiments, single base extension assays and oligonucleotide ligation assays are performed at single molecule levels to provide authentication. This level of authentication allows very high multiplexing and digital counting to quantify relative and absolute abundance with a higher accuracy previously unavailable via optical imaging.

Sequencing

Optical detection imaging systems may be diffraction-limited, and thus have a theoretical maximum resolution of approximately 300 nm with fluorophores typically used in sequencing. To date, the best sequencing systems have had center-to-center spacings between adjacent polynucleotides of approximately 600 nm on their arrays, or approximately 2× the diffraction limit. This factor of 2× is needed to account for intensity, array & biology variations that can result in errors in position. To achieve a $10 genome, an approximately 200 nm center to center spacing is required, which may require sub-diffraction-limited imaging capability.

For sequencing, the purpose of the system and methods described herein may be to resolve polynucleotides that are sequenced on a substrate with a center-to-center spacing below the diffraction limit of the optical system.

As described herein, we provide methods and systems to achieve sub-diffraction-limited imaging in part by identifying a position of each analyte with a high accuracy (e.g., 1 nm RMS or less). By comparison, state of the art Super Resolution systems can only identify location with an accuracy down to approximately 20 nm RMS-2× worse than this system. Thus, the methods and system disclosed herein may enable sub-diffraction limited-imaging to identify densely-packed molecules on a substrate to achieve a high data rate per unit of enzyme, data rate per unit of time, and high data accuracy. These sub-diffraction limited imaging techniques are broadly applicable to techniques using cycled detection as described herein.

Multiple Cycles of Sequencing

Methods of Making CATs

Creation of Circularized ssDNA Targets

In some embodiments, described herein are methods of preparing a library of concatemers to distribute as a layer onto the surface of a substrate, e.g., as randomly distributed, densely packed layer. To synthesize concatemers comprising target DNA to be sequenced, first, target DNA can be amplified and converted into circular DNA templates. In some embodiments, amplification products may undergo circular template ligation, which can be conducted via template mediated enzymatic ligation (e.g., T4 DNA ligase) or template-free ligation using special DNA ligases (e.g., CircLigase) to form a precursor to the concatemers formed via rolling circle amplification of the circular DNA templates.

RCA/RCR Basic Technique

Rolling circle replication may describe a process of unidirectional nucleic acid replication that can rapidly synthesize multiple copies of circular molecules of DNA or RNA.

RCA (rolling circle amplification) may be an isothermal nucleic acid amplification technique where the polymerase continuously adds single nucleotides to a primer annealed to a circular template which results in a long concatemer ssDNA that contains tens to hundreds of tandem repeats (complementary to the circular template).

Rolling circle amplification can be performed by exposing the circular DNA templates to: 1. A DNA polymerase. 2. A suitable buffer solution that is compatible with the polymerase. 3. A short DNA or RNA primer. 4. Deoxynucleotide triphosphates (dNTPs).

In some embodiments, the polymerase used in rolling circle amplification is Phi29, Bst, or Vent exo-DNA polymerase for DNA amplification, and T7 RNA polymerase for RNA amplification. RCA can be conducted at a constant temperature (room temperature to 37° C.) in both free solution and on top of deposited targets (solid phase amplification). A DNA RCA reaction typically proceeds via primer-induced single-strand DNA elongation.

In some embodiments, a method for constructing concatemer libraries of sequencing substrates to load onto a physical substrate, such as a flow cell, is shown inFIG.19. In some embodiments, concatemer libraries of sequencing substrates are constructed as shown inFIG.20. ‘Hairs’ may be ssDNA molecules that can be generated by using a reverse primer to synthesize in the opposite direction as the extending concatemer DNA. These ‘hairs’ can be used to control the size and/or exclusion properties of the concatemers. In some embodiments, the sequencing reaction described herein occurs using the ssDNA ‘hairs’ as templates.

Terminating RCR Reaction

The rolling circle amplification of the CAT can be stopped by the addition of EDTA to chelate the essential Mg2+co-factor of the phi29 enzyme. Phi29 is a strongly displacing polymerase, while the standard polymerases used for sequencing, for example Therminator 9, are only weakly displacing. A more displacing enzyme for sequencing this substrate may be used or adapted.

Alternatively, one may use single strand binding proteins (SSBs) or helicases, or combinations of them to aid in the displacement. These may be added to the extension reaction or used as pre-incubation operations to prepare the substrate for sequencing.

Alternatively, the rolling circle reaction may be stopped using an unlabeled reversible terminator. This may be a way to make the stoppage more uniform within the solution, yielding more uniform-sized CATs than stoppage with EDTA. Additionally, the sequencing reaction may then be initiated from the unblocking operation, followed by extension with labeled reversible terminator nucleotides. This may allow for the natural selection of substrates that where the extending 3′ end was accessible for the normal reactions of sequencing by synthesis.

The phi29 is likely very tightly bound to the extending end of the CAT. The use of a reversible terminator to stop the reaction may destabilize that interaction. Other protein denaturants like chaotropic salts or detergents may displace the phi29 to enable the sequencing reaction

The CATs have several identical copies of the target DNA on the extending single strand. CATs can also have several identical reverse copies of the target DNA on ssDNA ‘hairs’ generated as described above.

In some embodiments concatemers are at least 1,000 nucleotides in length (no more than, from 400,000).

In some embodiments, concatemers are at least 150 nm in diameter (no more than 300 nm). For example, the exclusion zone between adjacent concatemers is not less than the minimum center-to-center distance to achieve the predetermined density or pitch.

Methods of Making Arrays (Randomly Distributed Close Packed Layer of Concatemers)

Controlled Spacing

Provided herein are several mechanisms to control the distribution of minimum center-to-center distance between CATs arrayed on an un-patterned surface. In some embodiments, these methods and compositions may facilitate formation of a uniform, close-packed self-assembled random layer of CATs with a controlled minimum center-to-center distance between adjacent CATs such that they can be sequenced with minimal cross-talk between the dye-labeled sequencing substrates.

The CATs themselves are mutually repellant in solution due to their strong negative charge, but they may nonetheless be too close to each other for effective diffusion-limited resolution of labeled adjacent CATs once adsorbed to a surface.

In some embodiments, the concatemers may be ‘encased’ or ‘enveloped’ in a shell of a repellant or attractive substance to increase their effective exclusion size without altering the size of the CAT itself or the number of copies of the sequencing substrate they contain.

In some embodiments, a protein layer to which the CATs adsorb on the surface of the substrate may be modified to space the interacting proteins out on the surface. For example, the CATs can interact with the glass, silicon or modified (e.g. amino-silanated) surface through an interaction with proteins that have been previously adsorbed to the surface.

Thus, modifications of the CAT or the protein partner of the binding pair can assist in size exclusion to achieve a uniform, densely-packed layer of concatemers on a surface without specific attachment points for the CATs. In some embodiments, these modifications may include crosslinking or attaching molecules like PEG or polysaccharide to coat the CAT or its protein binding partner.

Shown inFIG.21Ais an embodiment depicting coated concatemers.

The inner core in this embodiment may be multiple copies of a DNA target that are entwined. The outer layer, e.g., the coating, can include compounds like PEG, compounds with zwitterionic features, ampholine ampholytes, sulphobetaine, and other similar molecules with the positive charges interacting with nucleic acid on the inside and negative charges on the outside the ensure the nanospheres do not clump.

Loading of CATs on the Chip

In some embodiments concatemers may be distributed onto an unpatterned surface of a substrate in a high-density layer. This close-packed formation facilitates formation of tightly packed sequencing substrates which may enable higher throughput and/or lower cost sequencing. In some embodiments, the surface may be patterned. An example of a densely packed concatemer layer on an unpatterned surface is shown inFIG.25.

In some embodiments, concatemers may be loaded on a biochip and closely packed to enable a center to center distance of −250 nm with a variance of +/−25 nm.

In some embodiments, the concatemers may comprise a coating to achieve a lower threshold of center-to-center distances between adjacent concatemers to minimize crosstalk during detection. In some embodiments, after binding the concatemers to the surface, the coating is dissolved, and the CATs attached to the surface and can be sequenced.

Another protein such as bovine serum albumin (BSA) may be used, either by chemically crosslinking to the CAT or the protein binding partner, or by attaching the spacer protein (e.g. BSA) to an oligonucleotide complementary to the common library adapter sequence through streptavidin interaction. Using BSA to coat the CAT may have the additional benefit of making a protein gel in the bound layer of CATs which may make the local environment for the enzymatic reaction more similar to the natural environment of the nucleus where polymerases act.

One may also be able to hybridize long single stranded oligonucleotides that are partially complementary to the common library adapter sequence and extend beyond that sequence without homology. In some embodiments, the long single stranded oligonucleotides may be the hairs mentioned above in Paragraph [00113]. Such long oligonucleotides may act to increase the size of the CAT without altering the number of sequencing substrates it contains. After surface attachment, these long oligonucleotides may be washed away, and each CAT may collapse towards the center of its attachment site, increasing the effective center to center distance between adjacent CATs.

DNA may also be used to modify the protein binding partner (by crosslinking or attachments such as strep-avidin) to create a surface that has attractive protein binding sites separated by repellant areas, for instance due to their negative charge.

Deposition of a Closely-Packed Concatemer Layer onto an Unpatterned Surface

One of the limitations to optimum packing density of biological analytes from an aqueous solution onto an un-patterned, adherent solid surface may be that the random binding of the analytes onto the surface does not provide for maximal close-packing due to the inability of the adhered analyte to move laterally and minimize spacing between bound molecules. As a result, this random irreversible sticking of analytes produces spacing defects in what may otherwise be arranged into a maximally close-packed array.

However, many biological analytes, including proteins and nucleic acids, may be known to be surface active and migrate to the air-water interface that results in a lowering of the surface tension at that interface, to produce a metastable monolayer of biomolecules. In this case, the surface-active analytes are free to move laterally at the interface and achieve a maximal close-packed density, with unfavorable hydrophobic interactions in solution being the driving force for maximal packing.

Therefore, in some embodiments, close-packed, spontaneously formed monolayer constructs of biomolecules at the air-water interface can be transferred or deposited onto a solid surface by pulling or dragging a bolus of the biomolecule solution across the solid surface that is already in contact with air. Thereby, the close-packed biomolecule construct at the air-water interface is deposited onto the solid surface from the point of three-phase (air-water solid) contact as the bolus moves across the solid surface.

In some embodiments, a protein layer may be laid down on the surface before the CATs are added. Then the CATs may be added to the already laid down protein layer. This sequential addition may be particularly effective if the binding protein is the modified partner.

Sequencing

Sequencing Work Flow

In some embodiments, provided herein are methods to detect the sequences of polynucleotides from the concatemers, e.g., through forming a densely-packed layer on an unpatterned surface and performing cycled sequencing by synthesis (see, e.g,FIG.23). In some embodiments, the surface may be patterned.

The detection of targets and their authentication based on repeat hybridizations may be a key feature enabling target identification and counting for quantification.

Syncing and Signal Calling (ddNTP Capping of Unreacted Oligonucleotides)

In some embodiments, the sequencing by synthesis may include the addition of an irreversible ddNTP terminator after an extension cycle to cap unextended oligonucleotides. For example, after getting maximal initiation and/or extension with a mixture of labeled and cold reversible terminators, a cycle of extension (e.g., with a different polymerase that can, better incorporate ddNTPs) and very high concentrations of all four ddNTPs may irreversibly terminate the extension of any sequencing template within a CAT that failed to extend at the cycle in question. Although this may lead to progressive loss of signal, proportional to the inefficiency of initiation or extension, it may also reduce background at subsequent cycles of those templates within the CAT that ‘skipped’ extension at any cycle, a process which results in mixed signal from lagging synthesis on some of the identical templates within the CAT.

This process may lead to increased synchronization of templates within a CAT, yielding less signal from lagging templates, so purer signal from the correct base in the sequence. All other things being equal, it may lead to longer effective sequence reads.

Reaction

The CATs may have several identical copies of the target DNA, but the last copy made during rolling circle amplification is unique in that it contains an actively extending 3′ end. This ssCircle and its actively extending end maybe near the center of the ball of DNA that is the CAT, so it is near the center of the exclusion zone within the monolayer of CATs. It is also away from the surface on which that monolayer is formed. Raising the actively extending end away from the surface may increase the accessibility for the chemicals and enzymes used in the sequencing reaction, and raise the dye labels above the focal plane of background fluorescence on the surface. These properties may make it ideal for single-molecule sequencing.

Paired End Sequencing

UMI Embodiment

Unique Molecular identifiers (UMIs) have been used to tag molecules to enable identification of duplicate PCR products and to enable double stranded sequencing applications that reduce error.

In some embodiments, adapters that contain UMIs may be incorporated into the circularized DNA template used to form the concatemer.

In one embodiment, UMI A1 and A2 adaptors may be added to the 5′ and 3′ ends of Strand A and B, as shown inFIG.24. A1 and A2 can have barcodes for sample ID. They also may have regions used for ligation/circle generation and sequencing primer binding regions to enable sequencing both strands. The adaptors may also have the UMI sequences.

After the completion of sequencing the UMIs can be used to locate circles emanating from the same DNA fragment and analyzed as paired end reads. Paired end reads are useful for mapping if the read lengths are short.

Although UMI may be used, many applications, such as NIPT, PCR amplified panels, and large portions of the genome can be reliably sequenced without having paired end capability.

Imaging and Cycled Detection

As described herein, each of the detection methods and systems may require cycled detection to achieve sub-diffraction limited imaging. Cycled detection includes the binding and imaging of probes, such as antibodies or nucleotides, bound to detectable labels that can emit a visible light optical signal. By using positional information from a series of images of a field from different cycles, deconvolution to resolve signals from densely packed substrates can be used effectively to identify individual optical signals from signals obscured due to the diffraction limit of optical imaging. After multiple cycles the precise location of the molecule may become increasingly more accurate. Using this information additional calculations can be performed to aid in crosstalk correction regarding known asymmetries in the crosstalk matrix occurring due to pixel discretization effects.

Antioxidant Solution for Nucleotide Detection

Some aspects of this disclosure may determine a relative position of an analyte deposited on a surface of a densely packed substrate. This surface may comprise either a patterned or unpatterned surface with one or a plurality of analytes deposited on the surface at discrete locations. An analyte may be a single molecule (of any molecular size), a single biomolecule, a polypeptide, a protein (folded or unfolded), a polynucleotide molecule (ribonucleic acid (RNA), complementary DNA (cDNA), or DNA), a fragment thereof, a modified molecule thereof, such as a modified nucleic acid, or a combination thereof. A target polynucleotide may comprise a hybridized primer to facilitate sequencing by synthesis.

A plurality of cycles of probe binding and signal detection on the surface may involve contacting an analyte with a plurality of probes from a probe set, where the probes comprise a detectable label and each probe binds specifically to a target analyte. The detectable label can be directly or indirectly bound to, hybridized to, conjugated to, or covalently linked to the probe. The detectable label may be a fluorescent moiety. The detectable label may be a fluorescent molecule or a chemiluminescent molecule. A detectable label may comprise any molecule bound to a probe that can generate a detectable optical signal when the probe is bound to a target analyte and imaged using an optical imaging system. An optical imaging system may be used to detect one or a plurality of optical signals from individual probes bound to an analyte at discrete locations on a surface. The optical signals may be from a fluorescent moiety of the individual probes bound to an analyte at discrete locations on a surface. The fluorescent moieties may be any part of the molecular structure of the probes that illuminates when the probe is bound to an analyte. An optical imaging system may require incident light (e.g laser light) of a wavelength specific for the fluorescent label, or the use of other suitable sources of illumination to excite the fluorophore. Fluorescent light emitted from the fluorophore may then be detected at the appropriate wavelength using a suitable detection system such as for example a Charge-Coupled-Device (CCD) camera, which can optionally be coupled to a magnifying device, a fluorescent imager, a microscope, or another imaging system. An imaging system may comprise an optical microscope, electron microscope, confocal microscope, telescope, or other imaging instrument. An imaging system may further comprise a software algorithm or a suite of microscope image processing techniques.

Imaging a field of a surface may be performed with an antioxidant solution. When imaging an analyte bound to a detectable label, such as a polynucleotide with a hybridized fluorescent or chemiluminescent primer, the brightness of an incorporated fluorophore may diminish at each cycle of nucleotide addition. The intense and repeated exposure to illumination used when reading incorporated fluorophores during optical sequencing, may cause light-induced damage to the nucleic acid templates. When a detection operation requires repeated or prolonged exposure to intense illumination, it may be advantageous to utilize an antioxidant solution to improve the quality of an optical signal and preserve the integrity of nucleic acid templates. Use of a solution which comprises one or more antioxidants may improve performance, increasing the number of nucleotide additions which can be accurately determined in a sequencing experiment. The inclusion of an antioxidant as an additive in the solution may increase the signal or prevent the loss of signal that otherwise occurs over successive cycles of nucleotide incorporation and may allow more cycles of sequencing to be achieved using the same sequencing templates. In some embodiments, the solution is a buffer. In some embodiments, the buffer is an imaging buffer.

Solutions containing antioxidants may show an improvement over corresponding solutions absent such antioxidants in preventing light-induced chemical artifacts in cycles of sequencing by synthesis based on detection of fluorescently labelled nucleotide analogues. The inclusion of antioxidants may prevent or reduce light-induced chemical reactions from damaging the integrity of the nucleic acid template and may allow accurate determination of the identity of the incorporated base over multiple cycles of nucleotide incorporation in a sequencing reaction. An additive, such as an antioxidant such as erythorbic acid, may be added to a solution used in nucleotide detection processes. This solution may improve methods of nucleic acid sequencing by incorporating this additive. Such a solution may use an erythorbic acid or glutathione additive to improve the efficiency of fluorescence-based multiple cycle nucleic acid sequencing reactions. In some embodiments, the solution is a buffer. In some embodiments, the buffer is an imaging buffer.

The solution additives described herein may be utilized in any applicable nucleic acid sequencing methods. An applicable nucleic acid sequencing method may involve methods of parallel sequencing of multiple templates located at distinct locations. Sequencing may take place on a solid support or with “clustered” arrays. The methods described herein, or any other known method of sequencing nucleic acid clusters may be adapted simply by including one or more antioxidants as additives in the solution used for the detection or imaging operations. The use of antioxidant solution additives in a detection operation may have advantages in the context of sequencing on clustered arrays using fluorescently labelled nucleotide analogues. The use of antioxidant solution additives in a detection operation may also be used in the context of sequencing templates on single molecule arrays of nucleic acid templates. The solution additives described herein may extend to any nucleic acid detection technique which uses fluorescent labels. An analyte such as a template nucleic acid may be irradiated in the presence of an antioxidant detection solution such that the identity of one or more incorporated nucleotides may be determined. In some embodiments, the solution is a buffer. In some embodiments, the buffer is an imaging buffer.

The one or more antioxidants may be present in the solution at a concentration of about at least 1 millimolar (mM), 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 15 mM, 20 mM, 30 mM, 40 mM, 50 mM, 60 mM, 70 mM, 80 mM, 90 mM, 100 mM or more. For example, the one or more antioxidants may be present in the solution at a concentration in the range of from 10 to 100 mM, preferably 20 to 50 mM. The one or more antioxidants may be present in the solution at a concentration of about at most 100 mM, 90 mM, 80 mM, 70 mM, 60 mM, 50 mM, 40 mM, 30 mM, 20 mM, 10 mM, 9 mM, 8 mM, 7 mM, 6 mM, 5 mM, 4 mM, 3 mM, 2 mM, 1 mM, or less than 1 mM. The one or more antioxidants may be present in the solution at a concentration of about 1 mM to 10 mM erythorbic acid and about 5 mM to 20 mM glutathione. Preferably, the one or more antioxidants may be present in the solution at a concentration of about 3 mM to 7 mM erythorbic acid and about 8 mM to 14 mM glutathione. In some embodiments, the solution is a buffer. In some embodiments, the buffer is an imaging buffer.

Those skilled in the art will readily recognize a variety of buffers that can be used in the solutions contemplated herein. Typical buffers include, but are not limited to, weak acids, weak bases, or mixtures thereof. As an example, the buffer components can be water soluble materials such as phosphoric acid, tartaric acids, lactic acid, succinic acid, citric acid, acetic acid, ascorbic acid, aspartic acid, glutamic acid, and salts thereof. Acceptable buffering agents include, for example, a Tris buffer tris(hydroxymethyl)aminomethane) or (2-amino-2-(hydroxymethyl)propane-1,3-diol (Tris); N-(2-Hydroxyethyl)piperazine-N′-(2-ethanesulfonic acid) (HEPES); 2-(N-Morpholino)ethanesulfonic acid (MES); 2-(N-Morpholino)ethanesulfonic acid sodium salt (MES); 3-(N-Morpholino)propanesulfonic acid (MOPS); N-tris[Hydroxymethyl]methyl-3-aminopropanesulfonic acid (TAPS); 2-(bis(2-hydroxyethyl)amino)acetic acid (Bicine); 3-[N-tris(hydroxymethyl)methylamino]-2-hydroxypropanesulfonic acid (TAPSO); -[[1,3-dihydroxy-2-(hydroxymethyl)propan-2-yl]amino]ethanesulfonic acid (TES); and piperazine-N,N′-bis(2-ethanesulfonic acid) (PIPS).

Any buffering agent may be used in the buffering solution. An example of an appropriate buffering agent may be tris (tris (hydroxymethyl)aminomethane) (Tris-HCl). Additionally, salts, e.g. sodium chloride or any other convenient salt, may be present at a concentration of at least about 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 15 mM, 20 mM, 30 mM, 40 mM, 50 mM, 60 mM, 70 mM, 80 mM, 90 mM, 100 mM, or more than 100 mM. Salts may be present at a concentration of less than about 100 mM, 90 mM, 80 mM, 70 mM, 60 mM, 50 mM, 40 mM, 30 mM, 20 mM, 10 mM, 9 mM, 8 mM, 7 mM, 6 mM, 5 mM, 4 mM, 3 mM, 2 mM, 1 mM, or less than 1 mM. The buffering agent may be present in the solution at a concentration of about 10 mM to 30 mM Tris-HCl. Preferably, the buffering agent may be present in the solution at a concentration of about 15 mM to 25 mM Tris-HCl. An example of a solution which may be used in all methods comprises 20 mM Tris-HCl, 10 mM Glutathione, and 5 mM Erythorbic Acid at a pH of 8.

In some embodiments, the solution can comprise a pH of about 7 to about 9.2. In some embodiments, the solution can comprise a pH of about 7 to about 7.2, about 7 to about 7.4, about 7 to about 7.6, about 7 to about 7.8, about 7 to about 8, about 7 to about 8.2, about 7 to about 8.4, about 7 to about 8.6, about 7 to about 8.8, about 7 to about 9, about 7 to about 9.2, about 7.2 to about 7.4, about 7.2 to about 7.6, about 7.2 to about 7.8, about 7.2 to about 8, about 7.2 to about 8.2, about 7.2 to about 8.4, about 7.2 to about 8.6, about 7.2 to about 8.8, about 7.2 to about 9, about 7.2 to about 9.2, about 7.4 to about 7.6, about 7.4 to about 7.8, about 7.4 to about 8, about 7.4 to about 8.2, about 7.4 to about 8.4, about 7.4 to about 8.6, about 7.4 to about 8.8, about 7.4 to about 9, about 7.4 to about 9.2, about 7.6 to about 7.8, about 7.6 to about 8, about 7.6 to about 8.2, about 7.6 to about 8.4, about 7.6 to about 8.6, about 7.6 to about 8.8, about 7.6 to about 9, about 7.6 to about 9.2, about 7.8 to about 8, about 7.8 to about 8.2, about 7.8 to about 8.4, about 7.8 to about 8.6, about 7.8 to about 8.8, about 7.8 to about 9, about 7.8 to about 9.2, about 8 to about 8.2, about 8 to about 8.4, about 8 to about 8.6, about 8 to about 8.8, about 8 to about 9, about 8 to about 9.2, about 8.2 to about 8.4, about 8.2 to about 8.6, about 8.2 to about 8.8, about 8.2 to about 9, about 8.2 to about 9.2, about 8.4 to about 8.6, about 8.4 to about 8.8, about 8.4 to about 9, about 8.4 to about 9.2, about 8.6 to about 8.8, about 8.6 to about 9, about 8.6 to about 9.2, about 8.8 to about 9, about 8.8 to about 9.2, or about 9 to about 9.2. In some embodiments, the solution can comprise a pH of about 7, about 7.2, about 7.4, about 7.6, about 7.8, about 8, about 8.2, about 8.4, about 8.6, about 8.8, about 9, or about 9.2. In some embodiments, the solution can comprise a pH of at least about 7, about 7.2, about 7.4, about 7.6, about 7.8, about 8, about 8.2, about 8.4, about 8.6, about 8.8, or about 9. In some embodiments, the solution can comprise a pH of at most about 7.2, about 7.4, about 7.6, about 7.8, about 8, about 8.2, about 8.4, about 8.6, about 8.8, about 9, or about 9.2.

The solutions described herein may be of substantially similar composition to a detection/imaging solution typically used in the chosen detection/imaging technique, except for the addition of the antioxidant component(s). The solution may contain other reaction components such as enzymes, enzyme cofactors, dNTPS etc. if the presence of these components is compatible with the particular detection/imaging technique for which the solution is intended to be used. For methods involving nucleic acid synthesis, such as sequencing by synthesis, the same reaction solution may be used for the nucleotide incorporation operations and for the detection operations, with no intermediate washing operation. The solution may also comprise one or more nucleotides required for the nucleic synthesis reaction and also a suitable polymerase enzyme. Buffers may be supplied as liquid concentrates requiring dilution prior to use. Solutions may also be supplied in the form of buffer tablets or solid “concentrates” to be dissolved in a suitable solvent prior to use in order to form the solution. Buffer concentrates or tablets may be supplied together with instructions setting out how the solution is to be diluted prior to use. In the case of buffer concentrates and buffer tablets the amount of antioxidant present in the solution refers to the amount present in the solution as it is correctly diluted or made up prior to use. In some embodiments, the solution is a buffer. In some embodiments, the buffer is an imaging buffer.

Methods for Optical Detection of Analytes

In some embodiments, optical signals may be digitized, and analytes are identified based on a code (ID code) of digital signals for each analyte.

As described herein, analytes are deposited to a solid substrate, and probes are bound to the analytes. Each of the probes may comprise tags and specifically bind to a target analyte. In some embodiments, the tags may be fluorescent molecules that emit the same fluorescent color, and the signals for additional fluorophores are detected at each subsequent pass. During a pass, a set of probes comprising tags may be contacted with the substrate allowing them to bind to their targets. An image of the substrate may be captured, and the detectable signals analyzed from the image obtained after each pass. The information about the presence and/or absence of detectable signals may be recorded for each detected position (e.g., target analyte) on the substrate.

In some embodiments, the present disclosure may comprise methods that include operations for detecting optical signals emitted from the probes comprising tags, counting the signals emitted during multiple passes and/or multiple cycles at various positions on the substrate, and analyzing the signals as digital information using a K-bit based calculation to identify each target analyte on the substrate. Error correction can be used to account for errors in the optically-detected signals, as described below.

In some embodiments, a substrate may be bound with analytes comprising N target analytes. To detect N target analytes, M cycles of probe binding and signal detection may be chosen. Each of the M cycles may include 1 or more passes, and each pass may include N sets of probes, such that each set of probes specifically binds to one of the N target analytes. In certain embodiments, there may be N sets of probes for the N target analytes.

In some embodiments, a cycle comprises one or more sequencing reactions. In some embodiments, the one or more sequencing reactions comprise one or more probes binding to one or more analytes. In some embodiments, the one or more probes comprise one or more blocking groups. In some embodiments, when a probe is bound to an analyte, the probe binding reaction is terminated by the one or more blocking groups. After probe binding, the unincorporated nucleotides are removed from the flow-cell by washing and the bound probes are imaged to identify the analyte. After the images are captured, the detectable label and blocking group are cleaved from the analyte using a cleaving solution, allowing subsequent addition of another probe in a subsequent cycle. In some embodiments, the cleaving solution comprises 150 mM TCEP ((tris(2-carboxyethyl)phosphine) and 40-50 mM THPP (Tris(hydroxypropyl)phosphine) at pH9.0). This extension, detection and cleavage cycle is then repeated to increase the read length. In some embodiments, the analytes are nucleotides. In some embodiments, the probes are individual nucleic acid molecules.

In some embodiments, the cleaving solution comprises TCEP having a concentration of about 10 mM to about 100 mM. In some embodiments, the cleaving solution comprises TCEP having a concentration of about 10 mM to about 20 mM, about 10 mM to about 30 mM, about 10 mM to about 40 mM, about 10 mM to about 50 mM, about 10 mM to about 60 mM, about 10 mM to about 70 mM, about 10 mM to about 80 mM, about 10 mM to about 90 mM, about 10 mM to about 100 mM, about 20 mM to about 30 mM, about 20 mM to about 40 mM, about 20 mM to about 50 mM, about 20 mM to about 60 mM, about 20 mM to about 70 mM, about 20 mM to about 80 mM, about 20 mM to about 90 mM, about 20 mM to about 100 mM, about 30 mM to about 40 mM, about 30 mM to about 50 mM, about 30 mM to about 60 mM, about 30 mM to about 70 mM, about 30 mM to about 80 mM, about 30 mM to about 90 mM, about 30 mM to about 100 mM, about 40 mM to about 50 mM, about 40 mM to about 60 mM, about 40 mM to about 70 mM, about 40 mM to about 80 mM, about 40 mM to about 90 mM, about 40 mM to about 100 mM, about 50 mM to about 60 mM, about 50 mM to about 70 mM, about 50 mM to about 80 mM, about 50 mM to about 90 mM, about 50 mM to about 100 mM, about 60 mM to about 70 mM, about 60 mM to about 80 mM, about 60 mM to about 90 mM, about 60 mM to about 100 mM, about 70 mM to about 80 mM, about 70 mM to about 90 mM, about 70 mM to about 100 mM, about 80 mM to about 90 mM, about 80 mM to about 100 mM, or about 90 mM to about 100 mM. In some embodiments, the cleaving solution comprises TCEP having a concentration of about 10 mM, about 20 mM, about 30 mM, about 40 mM, about 50 mM, about 60 mM, about 70 mM, about 80 mM, about 90 mM, or about 100 mM. In some embodiments, the cleaving solution comprises TCEP having a concentration of at least about 10 mM, about 20 mM, about 30 mM, about 40 mM, about 50 mM, about 60 mM, about 70 mM, about 80 mM, or about 90 mM. In some embodiments, the cleaving solution comprises TCEP having a concentration of at most about 20 mM, about 30 mM, about 40 mM, about 50 mM, about 60 mM, about 70 mM, about 80 mM, about 90 mM, or about 100 mM.

In some embodiments, the cleaving solution comprises THPP having a concentration of about 51 to about 60. In some embodiments, the cleaving solution comprises THPP having a concentration of about 51 to about 52, about 51 to about 53, about 51 to about 54, about 51 to about 55, about 51 to about 56, about 51 to about 57, about 51 to about 58, about 51 to about 59, about 51 to about 60, about 52 to about 53, about 52 to about 54, about 52 to about 55, about 52 to about 56, about 52 to about 57, about 52 to about 58, about 52 to about 59, about 52 to about 60, about 53 to about 54, about 53 to about 55, about 53 to about 56, about 53 to about 57, about 53 to about 58, about 53 to about 59, about 53 to about 60, about 54 to about 55, about 54 to about 56, about 54 to about 57, about 54 to about 58, about 54 to about 59, about 54 to about 60, about 55 to about 56, about 55 to about 57, about 55 to about 58, about 55 to about 59, about 55 to about 60, about 56 to about 57, about 56 to about 58, about 56 to about 59, about 56 to about 60, about 57 to about 58, about 57 to about 59, about 57 to about 60, about 58 to about 59, about 58 to about 60, or about 59 to about 60. In some embodiments, the cleaving solution comprises THPP having a concentration of about 51, about 52, about 53, about 54, about 55, about 56, about 57, about 58, about 59, or about 60. In some embodiments, the cleaving solution comprises THPP having a concentration of at least about 51, about 52, about 53, about 54, about 55, about 56, about 57, about 58, or about 59. In some embodiments, the cleaving solution comprises THPP having a concentration of at most about 52, about 53, about 54, about 55, about 56, about 57, about 58, about 59, or about 60.

In some embodiments, the use of both THPP and TCEP increases mapped yield (density of reads per square micron) and decreases error rate. In some embodiments, TCEP and THPP cleave and reduce the azido methyl blocker during SBS chemistry. In some embodiments, TCEP and THPP cleave and reduce the dye linker during SBS chemistry. In some embodiments, as TCEP is more readily oxidized by dissolved oxygen it exhibits a reduced stability in formulation. In some embodiments, such reduced stability poses risks for shipping and during the sequencing runs. Further, in some embodiments, as THPP is a more potent reducing agent, it is effective at lower concentrations. Additionally, in some embodiments, THPP is smaller and uncharged and is thus less sterically and electrostatically hindered with regards to reduction of the azido group in the context of the charged DNA/enzyme complex at the surface.

FIG.30shows the relationship between maximum density and read lengths for various concentrations of THPP.FIG.31shows the densities of high quality mapping reads with different cleave-buffer formulations. Therein, the mapped density for 27 fields by condition are at 40 bases (left half) and 100 bases (right half). As seen, at 40 bases there is a slight improvement in density with the addition of THPP. Also as seen, at 100 bases, there is a substantial improvement of mapped density with THPP.

FIG.32shows the relationship between error rate for 40 base reads per cycle for various concentrations of THPP.FIG.33shows the relationship between error rate for 100 base reads per cycle for various concentrations of THPP. As shown inFIGS.32and33, the addition of THPP leads to a substantially decreased error rate.

In each cycle, there may be a predetermined order for introducing the sets of probes for each pass. In some embodiments, the predetermined order for the sets of probes may be a randomized order. In other embodiments, the predetermined order for the sets of probes may be a non-randomized order. In one embodiment, the non-random order can be chosen by a computer processor. The predetermined order may be represented in a key for each target analyte. A key may be generated that includes the order of the sets of probes, and the order of the probes may be digitized in a code to identify each of the target analytes.

In some embodiments, each set of ordered probes may be associated with a distinct tag for detecting the target analyte, and the number of distinct tags may be less than the number of N target analytes. In that case, each N target analyte may be matched with a sequence of M tags for the M cycles. The ordered sequence of tags may be associated with the target analyte as an identifying code.

Quantification of Optically-Detected Probes

After the detection process, the signals from each probe pool may be counted, and the presence or absence of a signal and the color of the signal can be recorded for each position on the substrate.

From the detectable signals, K bits of information may be obtained in each of M cycles for the N distinct target analytes. The K bits of information may be used to determine L total bits of information, such that K×M=L bits of information and L≥log 2 (N). The L bits of information may be used to determine the identity (and presence) of N distinct target analytes. If one cycle (M=1) is performed, then K×1=L. However, multiple cycles (M>1) can be performed to generate more total bits of information L per analyte. Each subsequent cycle provides additional optical signal information that may be used to identify the target analyte.

In practice, errors in the signals occur, and this confounds the accuracy of the identification of target analytes. For instance, probes may bind the wrong targets (e.g., false positives) or fail to bind the correct targets (e.g., false negatives). Methods are provided, as described below, to account for errors in optical and electrical signal detection.

Electrical Detection Methods

In other embodiments, electrical detection methods may be used to detect the presence of target analytes on a substrate. Target analytes are tagged with oligonucleotide tail regions and the oligonucleotide tags are detected using ion-sensitive field-effect transistors (ISFET, or a pH sensor), which measures hydrogen ion concentrations in solution. ISFETs are described in further detail in U.S. Pat. No. 7,948,015, filed on Dec. 14, 2007, to Rothberg et al., and U.S. Publication No. 2010/0301398, filed on May 29, 2009, to Rothberg et al., which are both incorporated by reference in their entireties.

ISFETs present a sensitive and specific electrical detection system for the identification and characterization of analytes. In one embodiment, the electrical detection methods disclosed herein may be carried out by a computer (e.g., a processor). The ionic concentration of a solution can be converted to a logarithmic electrical potential by an electrode of an ISFET, and the electrical output signal can be detected and measured.

ISFETs have previously been used to facilitate DNA sequencing. During the enzymatic conversion of single-stranded(ss) DNA into double-stranded DNA, hydrogen ions may be released as each nucleotide is added to the DNA molecule. An ISFET may detect these released hydrogen ions and can determine when a nucleotide has been added to the DNA molecule. By synchronizing the incorporation of the nucleoside triphosphate (dATP, dCTP, dGTP, and dTTP), the DNA sequence may also be determined. For example, if no electrical output signal is detected when the single-stranded DNA template is exposed to dATP's, but an electrical output signal is detected in the presence of dGTP's, the DNA sequence may be composed of a complementary cytosine base at the position in question.

In one embodiment, an ISFET may be used to detect a tail region of a probe and then identify corresponding target analyte. For example, a target analyte can be deposited on a substrate, such as an integrated-circuit chip that contains one or more ISFETs. When the corresponding probe (e.g., aptamer and tail region) is added and specifically binds to the target analyte, nucleotides and enzymes (polymerase) may be added for transcription of the tail region. The ISFET may detect the release hydrogen ions as electrical output signals and measure the change in ion concentration when the dNTP's are incorporated into the tail region. The amount of hydrogen ions released may correspond to the lengths and stops of the tail region, and this information about the tail regions can be used to differentiate among various tags.

The simplest type of tail region may be one composed entirely of one homopolymeric base region. In this case, there are four possible tail regions: a poly-A tail, a poly-C tail, a poly-G tail, and a poly-T tail. However, it is often desirable to have diversity in tail regions.

One method of generating diversity in tail regions is by providing stop bases within a homopolymeric base region of a tail region. A stop base is a portion of a tail region comprising at least one nucleotide adjacent to a homopolymeric base region, such that the at least one nucleotide may be composed of a base that is distinct from the bases within the homopolymeric base region. In one embodiment, the stop base may be one nucleotide. In other embodiments, the stop base may comprise a plurality of nucleotides. Generally, the stop base is flanked by two homopolymeric base regions. In an embodiment, the two homopolymeric base regions flanking a stop base may be composed of the same base. In another embodiment, the two homopolymeric base regions may be composed of two different bases. In another embodiment, the tail region contains more than one stop base.

In one example, an ISFET can detect a minimum threshold number of 100 hydrogen ions. Target Analyte 1 may be bound to a composition with a tail region composed of a 100-nucleotide poly-A tail, followed by one cytosine base, followed by another 100-nucleotide poly-A tail, for a tail region length total of 201 nucleotides. Target Analyte 2 may be bound to a composition with a tail region composed of a 200-nucleotide poly-A tail. Upon the addition of dTTP's and under conditions conducive to polynucleotide synthesis, synthesis on the tail region associated with Target Analyte 1 may release 100 hydrogen ions, which can be distinguished from polynucleotide synthesis on the tail region associated with Target Analyte 2, which may release 200 hydrogen ions. The ISFET may detect a different electrical output signal for each tail region. Furthermore, if dGTP's are added, followed by more dTTP's, the tail region associated with Target Analyte 1 may then release one, then 100 more hydrogen ions due to further polynucleotide synthesis. The distinct electrical output signals generated from the addition of specific nucleoside triphosphates based on tail region compositions may allow the ISFET to detect hydrogen ions from each of the tail regions, and that information can be used to identify the tail regions and their corresponding target analytes.

Various lengths of the homopolymeric base regions, stop bases, and combinations thereof can be used to uniquely tag each analyte in a sample. Additional descriptions about electrical detection of aptamers and tail regions to identify target analytes in a substrate are described in U.S. Provisional Application No. 61/868,988, which is incorporated by reference in its entirety.

In other embodiments, antibodies are used as probes in the electrical detection method described above. The antibodies may be primary or secondary antibodies that bind via a linker region to an oligonucleotide tail region that acts as tag.

These electrical detection methods can be used for the simultaneous detection of hundreds (or even thousands) of distinct target analytes. Each target analyte can be associated with a digital identifier, such that the number of distinct digital identifiers is proportional to the number of distinct target analytes in a sample. The identifier may be represented by a number of bits of digital information and is encoded within an ordered tail region set. Each tail region in an ordered tail region set may be sequentially made to specifically bind a linker region of a probe region that is specifically bound to the target analyte. Alternatively, if the tail regions are covalently bonded to their corresponding probe regions, each tail region in an ordered tail region set may be sequentially made to specifically bind a target analyte.

In one embodiment, one cycle may be represented by a binding and stripping of a tail region to a linker region, such that polynucleotide synthesis occurs and releases hydrogen ions, which may be detected as an electrical output signal. Thus, number of cycles for the identification of a target analyte may be equal to the number of tail regions in an ordered tail region set. The number of tail regions in an ordered tail region set may be dependent on the number of target analytes to be identified, as well as the total number of bits of information to be generated. In another embodiment, one cycle is represented by a tail region covalently bonded to a probe region specifically binding and being stripped from the target analyte.

The electrical output signal detected from each cycle may be digitized into bits of information, so that after all cycles have been performed to bind each tail region to its corresponding linker region, the total bits of obtained digital information can be used to identify and characterize the target analyte in question. The total number of bits may be dependent on a number of identification bits for identification of the target analyte, plus a number of bits for error correction. The number of bits for error correction may be selected based on the predetermined robustness and accuracy of the electrical output signal. Generally, the number of error correction bits may be 2 or 3 times the number of identification bits.

Decoding the Order and Identity of Detected Analytes

The probes used to detect the analytes may be introduced to the substrate in an ordered manner in each cycle. A key may be generated that encodes information about the order of the probes for each target analyte. The signals detected for each analyte can be digitized into bits of information. The order of the signals may provide a code for identifying each analyte, which can be encoded in bits of information.

In optical and electrical detection methods described above, errors can occur in binding and/or detection of signals. In some cases, the error rate can be as high as one in five (e.g., one out of five fluorescent signals is incorrect). This equates to one error in every five-cycle sequence. Actual error rates may not be as high as 20%, but error rates of a few percent are possible. In general, the error rate depends on many factors including the type of analytes in the sample and the type of probes used. In an electrical detection method, for example, a tail region may not properly bind to the corresponding probe region on an aptamer during a cycle. In an optical detection method, an antibody probe may not bind to its target or bind to the wrong target.

Additional cycles may be generated to account for errors in the detected signals and to obtain additional bits of information, such as parity bits. The additional bits of information may be used to correct errors using an error correcting code. In one embodiment, the error correcting code may be a Reed-Solomon code, which is a non-binary cyclic code used to detect and correct errors in a system. In other embodiments, various other error correcting codes can be used. Other error correcting codes include, for example, block codes, convolution codes, Golay codes, Hamming codes, BCH codes, AN codes, Reed-Muller codes, Gappa codes, Hadamard codes, Walsh codes, Hagelbarger codes, polar codes, repetition codes, repeat-accumulate codes, erasure codes, online codes, group codes, expander codes, constant-weight codes, tornado codes, low-density parity check codes, maximum distance codes, burst error codes, luby transform codes, fountain codes, and raptor codes. See Error Control Coding, 2nd Ed., S. Lin and DJ Costello, Prentice Hall, New York, 2004. Examples are also provided below that demonstrate the method for error-correction by adding cycles and obtaining additional bits of information.

One example of a Reed-Solomon (RS) code may include a RS (15,9) code with 4-bit symbols, where n=15, k=9, s=4, and t=3, and n=2s-1 and k=n-2t, “n” being the number of symbols, “k” being the number of data symbols, “s” being the size of each symbol in bits, and “t” being the number of errors that can be corrected, and “2t” being the number of parity symbols. There are nine data symbols (k=9) and six parity symbols (2t=6). If base-X numbers are used, and X=4, then each fluorescent color is represented by two bits (0 and 1). A pair of colors may be represented by a four-bit symbol that includes two high bits and two low bits.

Since base-4 was chosen, seven probe pools, or a sequence of seven colors, are used to identify each target analyte. This sequence is represented by 3½, 4-bit symbols. The remaining 5½ data symbols are set to zero. A Reed-Solomon RS (15,9) encoder then generates the six parity symbols, represented by 12 additional probe pools. Thus, a total of 19 probe pools (7+12) may be required to obtain error correction fort=3 symbols.

Monte Carlo simulations of error-correcting code performance may be performed assuming seven probe pools, to identify up to 16,384 distinct targets. Using these simulations, the maximum permissible raw error rate (associated with identifying a fluorescent label) to achieve a corrected error rate of 10-5 can be determined for different numbers of parity bits.

In some embodiments, a key may be generated that includes the expected bits of information associated with an analyte (e.g., the expected order of probes and types of signals for the analyte). These expected bits of information for a particular analyte may be compared with the actual L bits of information that are obtained from the target analyte. Using the Reed-Solomon approach, an allowance of up t errors in the signals can be tolerated in the comparison of the expected bits of information and the actual L bits of information.

In some embodiments, a Reed-Solomon decoder may be used to compare the expected signal sequence with an observed signal sequence from a particular probe. For example, seven probe pools may be used to identify a target analyte, the expected color sequence being BGGBBYY, represented by 14 bits. Additional parity pools may then be used for error correction. For example, six 4-bit parity symbols may be used.

Methods and systems using cycled probe binding and optical detection are described in US Publication No. 2015/0330974, Digital Analysis of Molecular Analytes Using Single Molecule Detection, published Nov. 19, 2015, and US Publication No. 2018/0252936, High Speed Scanning With Acceleration Tracking, published Sep. 6, 2018, are each incorporated herein by reference herein in its entirety.

In some embodiments, the raw images may be obtained using sampling that is at least at the Nyquist limit to facilitate more accurate determination of the oversampled image. Increasing the number of pixels used to represent the image by sampling in excess of the Nyquist limit (oversampling) may increase the pixel data available for image processing and display.

Theoretically, a bandwidth-limited signal can be perfectly reconstructed if sampled at the Nyquist rate or above it. The Nyquist rate may be defined as twice the highest frequency component in the signal. Oversampling improves resolution, reduces noise and helps avoid aliasing and phase distortion by relaxing anti-aliasing filter performance requirements. A signal is said to be oversampled by a factor of N if it is sampled at N times the Nyquist rate.

Thus, in some embodiments, each image may be taken with a pixel size no more than half the wavelength of light being observed. In some embodiments, a pixel size of less than about 200 nm×200 nm may be used in detection to achieve sampling at or above the Nyquist limit. In some embodiments, sampling at a frequency of at least the Nyquist limit during raw imaging of the substrate may be used to optimize the resolution of the system or methods described herein. This can be done in conjunction with the deconvolution methods and optical systems described herein to resolve features on a substrate below the diffraction limit with high accuracy.

Processing Images from Different Cycles

There are several barriers which may be overcome by the present disclosure to achieve sub-diffraction limited imaging.

Pixelation error may be present in raw images and prevents identification of information present from the optical signals due to pixelation. Sampling at least at the Nyquist frequency and generation of an oversampled image as described herein may each assist in overcoming pixilation error.

The point-spread function (PSF) of various molecules overlap because the PSF size is greater than the pixel size (below Nyquist) and because the center-to-center spacing may be so small that crosstalk due to spatial overlap occurs. Nearest neighbor e.g. variable regression (for center-to center crosstalk correction) can be used to help with deconvolution of multiple overlapping optical signals. But this can be improved if we know the relative location of each analyte on the substrate and have good alignment of images of a field. In some embodiments, machine learning (e.g. artificial intelligence or “A.I.”) can be used to help with deconvolution of multiple overlapping optical signals. In some embodiments, the machine learning processes input data over multiple cycles of probe binding and imaging to deconvolve further images.

After multiple cycles, the precise location of the molecule may become increasingly more accurate. Using this information, additional calculations can be performed to aid in deconvolution by correcting for known asymmetries in the spatial overlap of optical signals occurring due to pixel discretization effects and the diffraction limit. They can also be used to correct for overlap in emission spectrum from different emission spectrum.

Highly accurate relative positional information for each analyte can be achieved by overlaying images of the same field from different cycles to generate a distribution of measured peaks from optical signals of different probes bound to each analyte. This distribution can then be used to generate a peak signal that corresponds to a single relative location of the analyte. Images from a subset of cycles can be used to generate relative location information for each analyte. In some embodiments, this relative position information may be provided in a localization file.

The specific area imaged for a field for each cycle may vary from cycle to cycle. Thus, to improve the accuracy of identification of analyte position for each image, an alignment between images of a field across multiple cycles can be performed. From this alignment, offset information compared to a reference file can then be identified and incorporated into the deconvolution algorithms to further increase the accuracy of deconvolution and signal identification for optical signals obscured due to the diffraction limit. In some embodiments, this information is provided in a Field Alignment File.

Once relative positional information is accurately determined for analytes on a substrate and field images from each cycle are aligned with this positional information, analysis of each oversampled image using crosstalk and nearest neighbor regression can be used to accurately identify an optical signal from each analyte in each image. In some embodiments, a plurality of optical signals obscured by the diffraction limit of the optical system may be identified for each of a plurality of biomolecules deposited on a substrate and bound to probes comprising a detectable label. In some embodiments, the probes may be incorporated nucleotides and the series of cycles may be used to determine a sequence of a polynucleotide deposited on the array using sequencing by synthesis.

Simulations of Deconvolution Applied to Images

Molecular densities may be limited by crosstalk from neighboring molecules.FIG.3depicts simulated images of single analytes. This particular image is a simulation of a layer of analytes on a 600 nm pitch that has been processed with a 2× oversampled filter. Crosstalk into eight adjacent spots is averaged as a function of array pitch and algorithm type.

FIG.4is a series of images processed with multiple pitches and two variations of image processing algorithms, the first is a 2× oversampled image and the second is a 4× oversampled image with deconvolution, as described herein.FIG.5is the crosstalk analysis of these two types of image processing at pitches down to 200 nm. Acceptable crosstalk levels at or below 25% with 2× oversample may occur for pitches at or above 275 nm. Acceptable crosstalk levels at or below 25% with 4× deconvolution using the point spread function of the optical system may occur for pitches at or above 210 nm.

The physical size of the molecule may broaden the spot roughly half the size of the binding area. For example, for an 80 nm spot the pitch may be increased by roughly 40 nm. Smaller spot sizes may be used, but this may have the trade-off that fewer copies may be allowed and greater illumination intensity may be required. A single copy may provide the simplest sample preparation but requires the greatest illumination intensity.

Methods for sub-diffraction limit imaging discussed to this point may involve image processing techniques of oversampling, deconvolution and crosstalk correction. Described herein are methods and systems that may incorporate determination of the precise relative location analytes on the substrate using information from multiple cycles of probe optical signal imaging for the analytes. Using this information, additional calculations can be performed to aid in crosstalk correction regarding known asymmetries in the crosstalk matrix occurring due to pixel discretization effects.

Methods

In some embodiments, as shown inFIG.6, provided herein is a method for accurately determining a relative position of analytes deposited on the surface of a densely packed substrate. The method includes first providing a substrate comprising a surface, wherein the surface comprises a plurality of analytes deposited on the surface at discrete locations. Then, a plurality of cycles of probe binding and signal detection on said surface is performed. Each cycle of detection includes contacting the analytes with a probe set capable of binding to target analytes deposited on the surface, imaging a field of said surface with an optical system to detect a plurality of optical signals from individual probes bound to said analytes at discrete locations on said surface, and removing bound probes if another cycle of detection is to be performed. From each image, a peak location from each of said plurality of optical signals from images of said field from at least two (e.g., a subset) of said plurality of cycles is detected. The location of peaks for each analyte is overlaid, generating a cluster of peaks from which an accurate relative location of each analyte on the substrate is then determined.

In some embodiments, as shown inFIG.7, the accurate position information for analytes on the substrate is then used in a deconvolution algorithm incorporating position information (e.g., for identifying center-to-center spacing between neighboring analytes on the substrate) can be applied to the image to deconvolve overlapping optical signals from each of said images. In some embodiments, the deconvolution algorithm may include nearest neighbor variable regression for spatial discrimination between neighboring analytes with overlapping optical signals.

In some embodiments, as shown inFIG.8, the method of analyte detection may be applied for sequencing of individual polynucleotides deposited on a substrate.

In some embodiments, optical signals may be deconvolved from densely packed substrates as shown inFIG.11. The operations can be divided into four different sections as shown inFIG.9: 1) Image Analysis, which may include generation of oversampled images from each image of a field for each cycle, and generation of a peak file (e.g., a data set) including peak location and intensity for each detected optical signal in an image. 2) Generation of a Localization File, which may include alignment of multiple peaks generated from the multiple cycles of optical signal detection for each analyte to determining an accurate relative location of the analyte on the substrate. 3) Generation of a Field Alignment file, which may include offset information for each image to align images of the field from different cycles of detection with respect to a selected reference image. 4) Extract Intensities, which may use the offset information and location information in conjunction with deconvolution modeling to determine an accurate identity of signals detected from each oversampled image. The “Extract Intensities” operation can also include other error correction, such as previous cycle regression used to correct for errors in sequencing by synthesis processing and detection. The operations performed in each section are described in further detail below.

Under the image analysis operations shown inFIG.10AandFIG.11, the images of each field from each cycle may be processed to increase the number of pixels for each detected signal, sharpen the peaks for each signal, and identify peak intensities form each signal. This information may be used to generate a peak file for each field for each cycle that includes a measure of the position of each analyte (from the peak of the observed optical signal), and the intensity, from the peak intensity from each signal. In some embodiments, the image from each field may first undergo background subtraction to perform an initial removal of noise from the image. Then, the images may be processed using smoothing and deconvolution to generate an oversampled image, which includes artificially generated pixels based on modeling of the signal observed in each image. In some embodiments, the oversampled image can generate 4 pixels, 9 pixels, or 16 pixels from each pixel from the raw image.

Peaks from optical signals detected in each raw image or present in the oversampled image may be then identified and intensity and position information for each detected analyte placed into a peak file for further processing.

In some embodiments, N raw images may correspond to all images detected from each cycle and each field of a substrate or output into N oversampled images and N peak files for each imaged field. The peak file may comprise a relative position of each detected analyte for each image. In some embodiments, the peak file may also comprise intensity information for each detected analyte. In some embodiments, one peak file may be generated for each color and each field in each cycle. In some embodiments, each cycle may further comprise multiple passes, such that one peak file can be generated for each color and each field for each pass in each cycle. In some embodiments, the peak file may specify peak locations from optical signals within a single field.

In example embodiments, the peak file may include XY position information from each processed oversampled image of a field for each cycle. The XY position information may comprise estimated coordinates of the locations of each detected detectable label from a probe (such as a fluorophore) from the oversampled image. The peak file can also include intensity information from the signal from each individual detectable label.

Generation of an oversampled image may be used to overcome pixelation error to identify information present that cannot be extracted due to pixelation. Initial processing of the raw image by smoothing and deconvolution may help to provide more accurate information in the peak files so that the position of each analyte can be determined with higher accuracy, and this information subsequently can be used to provide a more accurate determination of signals obscured in diffraction limited imaging.

In some embodiments, the raw images may be obtained using sampling that is at least at the Nyquist limit to facilitate more accurate determination of the oversampled image. Increasing the number of pixels used to represent the image by sampling in excess of the Nyquist limit (oversampling) may increase the pixel data available for image processing and display.

Theoretically, a bandwidth-limited signal can be perfectly reconstructed if sampled at the Nyquist rate or above it. The Nyquist rate may be defined as twice the highest frequency component in the signal. Oversampling improves resolution, reduces noise and helps avoid aliasing and phase distortion by relaxing anti-aliasing filter performance requirements. A signal is said to be oversampled by a factor of N if it is sampled at N times the Nyquist rate.

Thus, in some embodiments, each image may be taken with a pixel size no more than half the wavelength of light being observed. In some embodiments, a pixel size of less than about 200 nm×200 nm may be used in detection to achieve sampling at or above the Nyquist limit.

Smoothing may use an approximating function to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena. In smoothing, the data points of a signal may be modified so individual points are reduced, and points that are lower than the adjacent points are increased leading to a smoother signal. Smoothing may be used herein to smooth the diffraction limited optical signal detected in each image to better identify peaks and intensities from the signal.

Although each raw image is diffraction limited, described herein are methods that may result in the collection of multiple signals from the same analyte from different cycles. An embodiment of this method is shown in the flowchart inFIG.10B. These multiple signals from each analyte may be used to determine a position much more accurately than the diffraction limited signal from each individual image. They can be used to identify molecules within a field at a resolution of less than 5 nm. This information may be then stored as a localization file, as shown inFIG.11. The highly accurate position information can then be used to greatly improve signal identification from each individual field image in combination with deconvolution algorithms, such as cross-talk regression and nearest neighbor variable regression.

As shown inFIG.11, the operations for generating a localization file may use the location information provided in the peak files to determine relative positions of a set of analytes on the substrate. In some embodiments, each localization file may contain relative positions from sets of analytes from a single imaged field of the substrate. The localization file may combine position information from multiple cycles to generate highly accurate position information for detected analytes below the diffraction limit.

In some embodiments, the relative position information for each analyte may be determined on average to less than a 10 nm standard deviation (e.g., RMS, or root mean square). In some embodiments, the relative position information for each analyte may be determined on average to less than a 10 nm 2× standard deviation. In some embodiments, the relative position information for each analyte may be determined on average to less than a 10 nm 3× standard deviation. In some embodiments, the relative position information for each analyte may be determined to less than a 10 nm median standard deviation. In some embodiments, the relative position information for each analyte may be determined to less than a 10 nm median 2× standard deviation. In some embodiments, the relative position information for each analyte may be determined to less than a 10 nm median 3× standard deviation.

From a subset of peak files for a field from different cycles, a localization file may be generated to determine a location of analytes on the array. As shown inFIG.11, in some embodiments, a peak file is first normalized using a point spread function to account for aberrations in the optical system. The normalized peak file can be used to generate an artificial normalized image based on the location and intensity information provided in the peak file. Each image is then aligned. In some embodiments, the alignment can be performed by correlating each image pair and performing a fine fit. Once aligned, position information for each analyte from each cycle can then be overlaid to provide a distribution of position measurements on the substrate. This distribution can be used to determine a single peak position that provides a highly accurate relative position of the analyte on the substrate. In some embodiments, a Poisson distribution is applied to the overlaid positions for each analyte to determine a single peak.

The peaks determined from at least a subset of position information from the cycles may then be recorded in a localization file, which may comprise a measure of the relative position of each detected analyte with an accuracy below the diffraction limit. As described, images from only subset of cycles may be needed to determine this information.

As shown inFIG.11, a normalized peak file from each field for each cycle and color and the normalized localization file can be used to generate offset information for each image from a field relative to a reference image of the field. This offset information can be used to improve the accuracy of the relative position determination of the analyte in each raw image for further improvements in signal identification from a densely packed substrate and a diffraction limited image. In some embodiments, this offset information can be stored as a field alignment file. In some embodiments, the position information of each analyte in a field from the combined localization file and field alignment file may be less than 10 nm RMS, less than 5 nm RMS, or less than 2 nm RMS.

In some embodiments, a field alignment file may be generated by alignment of images from a single field by determining offset information relative to a master file from the field. One field alignment file may be generated for each field. This file can be generated from all images of the field from all cycles, and may include offset information for all images of the field relative to a reference image from the field.

In some embodiments, before alignment, each peak file is normalized with a point spread function, followed by generation of an artificial image from the normalized peak file and Fourier transform of the artificial image. The Fourier transform of the artificial image of the normalized peak file may be then convolved with a complex conjugate of the Fourier transform of an artificial image from the normalized localization file for the corresponding field. This may be done for each peak file for each cycle. The resulting files may then undergo an inverse Fourier transform to regenerate image files, and the image files aligned relative to the reference file from the field to generate offset information for each image file. In some embodiments, this alignment may include a fine fit relative to a reference file.

The field alignment file thus may contain offset information for each oversampled image, and can be used in conjunction with the localization file for the corresponding field to generate highly accurate relative position for each analyte for use in the subsequent “Extract Intensities” operations.

As an example, where 20 cycles are performed on a field and one image is generated for each of 4 colors to be detected, thus generating 80 images of the field, one Field Alignment file is generated for all 80 images (20 cycles*4 colors) taken of the field. In some embodiments, the field alignment file contents may include: the field, the color observed for each image, the operation type in the cycled detection (e.g., binding or stripping), and the image offset coordinates relative to the reference image.

In some embodiments, during the alignment process XY “shifts” or “residuals” to align 2 images are calculated, and the process is repeated for remaining images, best fit residual to apply to all is calculated.

In some embodiments, residuals that exceed a threshold may be thrown out, and best fit re-calculated. This process may be repeated until all individual residuals are within the threshold

Each oversampled image may be deconvolved using the accurate position information from the localization file and the offset information from the field alignment file. An embodiment of the intensity extraction operation is shown inFIG.10CandFIG.11. The Point Spread Function (PSF) of various molecules may overlap because the center-to-center spacing is so small that the point-spread function of signals from adjacent analytes overlaps. Nearest neighbor variable regression in combination with the accurate analyte position information and/or offset information can be used to deconvolve signals from adjacent analytes that have a center-to-center distance that inhibits resolution due to the diffraction limit. The use of the accurate relative position information for each analyte may facilitate spatial deconvolution of optical signals from neighboring analytes below the diffraction limit. In some embodiments, the relative position of neighboring analytes is used to determine an accurate center-to-center distance between neighboring analytes, which can be used in combination with the point spread function of the optical system to estimate spatial cross-talk between neighboring analytes for use in deconvolution of the signal from each individual image. This may enable the use of substrates with a density of analytes below the diffraction limit for optical detection techniques, such as polynucleotide sequencing.

In certain embodiments, emission spectra may overlap between different signals (e.g. “cross-talk”). For example, during sequencing by synthesis, the four dyes used in the sequencing process may have some overlap in emission spectra.

In particular embodiments, a problem of assigning a color (for example, a base call) to different features in a set of images obtained for a cycle when crosstalk may occur between different color channels and when the crosstalk is different for different sets of images. Such a problem can be solved by cross-talk regression in combination with the localization and field alignment files for each oversampled image to remove overlapping emission spectrums from optical signals from each different detectable label used. This may further increase the accuracy of identification of the detectable label identity for each probe bound to each analyte on the substrate.

Thus, in some embodiments, identification of a signal and/or its intensity from a single image of a field from a cycle as disclosed herein uses the following features: 1) Oversampled Image—provides intensities and signals at defined locations. 2) Accurate Relative Location—Localization File (provides location information from information from at least a subset of cycles) and Field Alignment File (provides offset/alignment information for all images in a field). 3) Image Processing—Nearest Neighbor Variable Regression (spatial deconvolution) and Cross-talk regression (emission spectra deconvolution) using accurate relative position information for each analyte in a field. Accurate identification of probes (e.g., antibodies for detection or complementary nucleotides for sequencing) for each analyte.

Image Processing Simulations

The effects of the methods and systems disclosed herein may be illustrated in simulated cross-talk plots shown inFIG.12A,FIG.12B,FIG.13AandFIG.13B. For each of these figures, a cross-talk plot showing the intensity of emission spectrum correlated with one of four fluorophores at each detected analyte in a 10 um×10 um region is shown. Each axis corresponding to one of the four fluorophores extends to each corner of the plot. Thus, a spot located in the center of the plot may have equal contribution of intensity from all four fluorophores. Emission intensity detected from an individual fluorophore during an imaging cycle may be assigned to move the spot in a direction either towards X, Y; X, −Y; −X, Y; or −X, ¬Y. Thus, separation of populations of spots along these four axes may indicate a clear deconvolved signal from a fluorophore at an analyte location. Each simulation may be based on detection of 1024 molecules in a 10.075 um×10.075 um region, indicating a density of 10.088 molecules per micron squared, or an average center-to-center distance between molecules of about 315 nm. This may be correlated with an imaging region of about 62×62 pixels at a pixel size of less than about 200 nm×200 nm.

FIG.12Ashows the cross-talk plot of fluorophore intensity between the four fluorophores from optical signals detected from the raw image.FIG.12BandFIG.13Aeach shows the separation between the four fluorophores achieved by generating a 4× oversampled image, indicating the achievement of some removal of cross-talk at each analyte.FIG.13Bshows a cross-talk plot for the same imaging region but with deconvolution and nearest neighbor regression performed as shown inFIG.11and described herein. As compared withFIG.13AandFIG.12A, each analyte detected shows clear separation of its optical signal from the other fluorophores, indicating a highly accurate fluorophore identification for each analyte.

FIG.14AandFIG.14Bshow a simulated four-color composite of each detected 10.075 μm×10.075 um region as simulated above. This visually represents the clarity between analytes form the raw image (FIG.14A) and the image processed as described herein (FIG.14B).

Sequencing

The methods described above and inFIG.11may also facilitate sequencing by sequencing by synthesis using optical detection of complementary reversible terminators incorporated into a growing complementary strand on a substrate comprising densely packed polynucleotides. Thus, signals correlating with the sequence of neighboring polynucleotides at a center-to-center distance below the diffraction limit can be reliably detected using the methods and optical detection systems described herein. Image processing during sequencing can also include previous cycle regression based on clonal sequences repeated on the substrate or on the basis of the data itself to correct for errors in the sequencing reaction or detection. In some embodiments, the polynucleotides deposited on the substrate for sequencing are concatemers. A concatemer can comprise multiple identical copies of a polynucleotide to be sequenced. Thus, each optical signal identified by the methods and systems described herein can refer to a single detectable label (e.g., a fluorophore) from an incorporated nucleotide, or can refer to multiple detectable labels bound to multiple locations on a single concatemer, such that the signal is an average from multiple locations. The resolution that may occur may not be between individual detectable labels, but between different concatemers deposited to the substrate.

In some embodiments, molecules to be sequenced, single or multiple copies, may be bound to the surface using covalent linkages, by hybridizing to capture oligonucleotide on the surface, or by other non-covalent binding. The bound molecules may remain on the surface for hundreds of cycles and can be re-interrogated with different primer sets, following stripping of the initial sequencing primers, to confirm the presence of specific variants.

In one embodiment, the fluorophores and blocking groups may be removed using chemical reactions. In another embodiment, the fluorescent and blocking groups may be removed using UV light.

In one embodiment, the molecules to be sequenced may be deposited on reactive surfaces that have 50-100 nM diameters and these areas may be spaced at a pitch of 150-300 nM. These molecules may have barcodes, attached onto them for target deconvolution and a sequencing primer binding region for initiating sequencing. Buffers may contain appropriate amounts of DNA polymerase to enable an extension reaction. These may contain 10-100 copies of the target to be sequenced generated by any of the gene amplification methods available (PCR, whole genome amplification etc.)

In another embodiment, single target molecules, tagged with a barcode and a primer annealing site may be deposited on a 20-50 nM diameter reactive surface spaced with a pitch of 60-150 nM. The molecules may be sequenced individually.

In one embodiment, a primer may bind to the target and may be extended using one dNTP at a time with a single or multiple fluorophore (s); the surface may be imaged, the fluorophore may be removed and washed and the process repeated to generate a second extension. The presence of multiple fluorophores on the same dNTP may enable defining the number of repeats nucleotides present in some regions of the genome (2 to 5 or more).

In a different embodiment, following primer annealing, all four dNTPs with fluorophores and blocked 3′ hydroxyl groups may be used in the polymerase extension reaction, the surface may be imaged and the fluorophore and blocking groups removed and the process repeated for multiple cycles.

In another embodiment, the sequences may be inferred based on ligation reactions that anneal specific probes that ligate based on the presence of a specific nucleotides at a given position.

A random array may be used which may have improved densities over prior art random arrays using the techniques outlined above, however random arrays generally have 4× to 10× reduced areal densities of ordered arrays. Advantages of a random array may include a uniform, non-patterned surface for the chip and the use of shorter nucleic acid strands because there may be no need to rely on the exclusionary properties of longer strands.

Methods for Determination of Super-Resolved Analyte Location

Provided herein are methods for identifying an analyte of a plurality of analytes disposed on a surface of a substrate. In some embodiments, the method comprises: providing a substrate comprising a surface, wherein the surface comprises said plurality of analytes disposed on the surface and reagents for sequencing by synthesis, performing a plurality of cycles of probe binding to said plurality of analytes, identifying said detectable labels for a cycle of the plurality of cycles, and identifying said analyte from said identified detectable labels across said plurality of cycles.

In some embodiments, the surface comprises said plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module. In some embodiments, a cycle of said plurality of cycles comprises contacting said plurality of analytes with a plurality of probes. In some embodiments, a probe of said plurality of probes comprises a detectable label. In some embodiments, identifying said detectable labels for a cycle of the plurality of cycles comprises applying a correction based on a neighbor effect. In some embodiments, identifying said detectable labels for a cycle of the plurality of cycles comprises applying a correction based on a relative position of one or more analytes of said plurality of analytes. In some embodiments, identifying said detectable labels for a cycle of the plurality of cycles comprises applying a correction based on applying a correction based on a neighbor effect and on a relative position of one or more analytes of said plurality of analytes.

In some embodiments, said surface is patterned. In some embodiments, said surface is unpatterned. In some embodiments, said correction comprises use of a distance-dependent correction factor. In some embodiments, said correction comprises use of a pattern-dependent correction factor. In some embodiments, said use of the pattern-dependent correction factor comprises a determination of one or more relative positions of one or more analytes of said plurality of analytes and a determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes. In some embodiments, said one or more relative positions of said analytes and said one or more distances relative to a number of pixels between said relative positions of said analytes are applied to a reference pixel grid to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, said one or more distances relative to a number of pixels between one or more pixels adjacent to a relative position of a first analyte of said plurality of analytes and one or more pixels adjacent to a relative position of a second analyte of said plurality of analytes to determine one or more interfering optical signals derived from one or more neighboring analytes. In some embodiments, said determination of one or more relative positions of said analytes of said plurality of analytes and said determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes are applied to said neighboring effect of one or more adjacent analytes of said plurality of analytes to determine one or more interfering optical signals derived from said analyte, wherein said adjacent analytes are adjacent to said analytes of said plurality of analytes. In some embodiments, said relative position of said analyte of said plurality of analytes, said neighboring effect of an analyte of said plurality of analytes, or both are determined at least in part by use of a trained machine learning algorithm.

In some embodiments, the relative position of the analyte of the plurality of analytes, the neighboring effect of an analyte of the plurality of analytes, or both are determined at least in part by use of a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm may comprise neural network, convolutional neural network, random forest, supper vector machines, logistic regression, decision trees, linear regression, naïve bayes, k-means clustering, or any combination thereof. In some embodiments, the trained machine learning algorithm may comprise a combination or ensemble of machine learning algorithms.

In some embodiments, the machine learning algorithm may be trained by supervised learning, unsupervised learning, transfer learning or any combination thereof. In some embodiments, the machine learning algorithm may be trained with previously obtained images of patterns of relative spatial arrangement of analytes, as seen inFIG.37. Figure. 40 represent images of analytes where the position of the analytes with respect to one another has been determined with a machine learning algorithm previously trained with images of patterns of relative spatial arrangement of analytes to determine information and distances between analytes. In some instances, the machine learning algorithm may overlay the relative distance between analytes, as seen inFIG.40, as line profiles between the center of each detected analytes. In some cases, the machine learning algorithm may be trained on gray scale intensity or color channel data of the images of the patterned surface. In some embodiments, a plurality of machine learning models may be each color channel. For the patterned surfaces the number of analytes and the spacing of analytes may provide the training label. In some embodiments, the spacing of analytes may comprise the spacing of pattern analytes described elsewhere herein. Alternatively, in some embodiments, the machine learning algorithm may be trained with images of analytes on un-patterned surfaces. In some cases, the machine learning algorithm may be trained on gray scale intensity or color channel data of the images of the un-patterned surface. In some embodiments, a plurality of machine learning models may be each trained on one of a plurality of color channels. The associated training label of the un-patterned surfaces may comprise the number of analytes determined by nearest neighbor deconvolution methods described elsewhere herein. In some embodiments, training the machine learning algorithm may comprise a validation step, where a validation dataset may be used to validate the trained machine learning algorithm. In some embodiments, the validation step may comprise k-fold cross-validation.

In some embodiments, said analytes are DNA concatemers. In some embodiments, said DNA concatemers are hybridized to ssDNA hairs. In some embodiments, said analytes are proteins or peptides. In some embodiments, said probes comprise a plurality of reversible terminator nucleotides. In some embodiments, said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label. In some embodiments, said resolving comprises removing interfering optical signals from a neighboring polynucleotide using a center-to-center distance between said neighboring polynucleotides from said determined relative positions. In some embodiments, said resolving function comprises deconvolution. In some embodiments, said polynucleotides are densely packed on said substrate such that there is overlap between optical signals emitted by said detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein said adjacent polynucleotides each comprise a distinct sequence.

In some embodiments, said relative position of said analytes deposited to the surface of the substrate is determined within 10 nm RMS.

In some embodiments, a ratio of the first to second analyte label intensities (i.e. molecule map) is used to determine super-resolved analyte location. In some embodiments, the location is determined where the signal-to-noise ratio (SNR) of the analyte on a substrate is at its best. SNR is calculated herein as (I1-I2)/I1 I1=brightest, 12=second brightest. In some embodiments the intensities of the different colors are corrected in various ways such as by normalization of the different intensities and/or correction for phasing issues. A super-resolved analyte location is determined pixel by pixel over a deconvolved image. In some embodiments, the analyte boundaries are determined by the Voronoi space of the analyte centers. In some embodiments, the precision of such location calculations lies below the diffraction limit. At each analyte center, the SNR is determined, and the base is called with a calculated confidence. In some embodiments non-linear filtering (e.g. sigmoid filtering) is performed to distinguish further real analytes. In some embodiments, the color is called for each individual pixel. In some embodiments, the methods and systems herein further extract line profiles between analyte centers.

In some embodiments, the intensity of the different colors is a raw intensity. In some embodiments, the intensity of the different colors is a corrected intensity. In some embodiments, the corrected intensity comprises normalization of the different colors. In some embodiments, the corrected intensity comprises subtraction of background signals. In some embodiments, the corrected intensity comprises a correction for phasing issues.

The SNR is influenced by the neighboring analyte intensities. For lower density patterned molecule arrays, the neighbor influence is distance dependent. For higher density analyte arrays especially unpatterned arrays, the neighbor influence can vary considerably, e.g. possibly due to imprecision of molecule location information. However, as analytes can be resolved below the diffraction limit using the devices, systems, and methods herein such neighbor intensity influences can be determined at high resolution.

In some embodiments, an analyte of interest may be adjacent to one or more neighboring analytes. In some embodiments, the neighboring analytes call the same color (e.g. have the same highest intensities) and are on opposing sides of the analyte of interest. In some embodiments, the analyte of interest calls a different color than the neighboring analytes. In some embodiments, the call of the color of the analyte of interest, with its low SNR, can be misinterpreted due to the influence of the color intensities of the neighboring analytes.

In some embodiments calling the color of the analyte of interest comprises extracting 4-color line intensity profiles at one or more cycles of probe-binding. In some embodiments, the peak color intensity is measured for any given analyte of interest. In some embodiments, a peak color intensity for a given analyte of interest is measured over one or more cycles. In some embodiments, a confidence level of a color-call is compared to the confidence level of color-calls for the analyte of interest for various cycles.

A non-limiting example of a 4-color line intensity profile extraction can be seen inFIGS.41A and41Brespectively. As seen, the vertical black line on the left-hand side of each panel indicates the analyte of interest's center position and the right-hand side vertical line of each panel indicates the neighboring analyte's position. In cycle 1 the analyte of interest calls green as the green intensity is clearly higher at the right-hand side vertical line than any other color. In cycle 4, however, the analyte of interest calls red, but with low confidence, as the red and green intensities are almost equal. The line profiles show that the real peak at the analyte of interest is green, but that the intensities of neighboring analytes (with red peaks at the right-hand side vertical line) interfere with the calling the correct color.

As such, in some embodiments, the methods and/or systems described herein employ one or more of a distance-dependent correction factor, a pattern-dependent correction factor, and machine learning.

In some embodiments, the distance-dependent correction factor is a form of nearest-neighbor analysis. One non-limiting example of a distance-dependent correction factor is an exponential decay function.

In some embodiments, the pattern-dependent correction factor is based on a relative geometric position between analytes. In some embodiments, the pattern-dependent correction is determined by detecting neighbors with a direct physical boundary to an analyte of interest, and/or determining neighbor distances. In some embodiments, the physical boundaries are determined using Voronoi space. In some embodiments, Voronoi space determines the physical boundaries using active yet static geometry based on analyte centers. In some embodiments, the neighbor distances are determined using Delaunay triangulation. In some embodiments, Delaunay triangulation employs active yet static geometry based on analyte centers. In some embodiments, the pattern-dependent correction employs an N×N pixel grid for passive yet dynamic geometry, whereas in some embodiments, the pattern-dependent correction is based on the 8 or more additional pixels surrounding a pixel representing the determined analyte center. Such corrections enable passive yet dynamic geometry.

In some embodiments, the machine learning algorithm employs a direct analyte environment as features to train machine-learning classifiers. Non-limiting examples of color-calling improvement when machine learning is utilized are depicted inFIGS.42and43, respectively.

Systems for Determination of Super-Resolved Molecule Location

Another aspect provided herein is a system for identifying an analyte of a plurality of analytes disposed on a surface of a substrate. In some embodiments, the system comprises a substrate, an optical imaging device, and an imaging processing module In some embodiments, the substrate comprises a surface. In some embodiments, the surface comprises a plurality of analytes. In some embodiments, the analytes are disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module. In some embodiments, the surface comprises reagents for sequencing by synthesis. In some embodiments, the optical imaging device is configured to perform a plurality of cycles of probe binding to said plurality of analytes. In some embodiments, one or more cycles of said plurality of cycles comprises contacting said plurality of analytes with a plurality of probes. In some embodiments, the probe comprises a detectable label. In some embodiments, the image processing module is configured to identify said detectable labels a cycle of the plurality of cycles, and identify said analytes disposed on the surface of the substrate from said identified detectable labels across said plurality of cycles. In some embodiments, said identifying comprises applying a correction based on a neighbor effect and a relative position of one or more analytes of said plurality of analytes.

In some embodiments, the systems described herein provide systems for implementing the methods described herein. In some embodiments, the systems comprise a substrate. In some embodiments, the surface of the substrate is patterned. In some embodiments, the surface of the substrate is unpatterned. In some embodiments, the system comprises one or more reagents. In some embodiments, the reagents comprise the probes described herein. In some embodiments, the reagents comprise the buffers described herein. In some embodiments, the reagents comprise the imaging buffers described herein. In some embodiments, the reagents comprise the cleaving solutions described herein. In some embodiments, the reagents comprise washing buffers/solutions. In some embodiments, the systems comprise one or more imaging modules. In some embodiments, the systems comprise a dispenser to dispense to the reagents described herein. In some embodiments, a single dispenser is used to dispense the reagents described herein. The single dispenser is washed with a washing buffer after every dispensing action. In some embodiments, the systems comprise a plurality of dispensers to dispense the reagents described herein. A dispenser of the plurality of dispensers may dispense only a single type of the reagents described herein.

Computer Systems

The present disclosure provides computer systems that are programmed to implement methods of the disclosure.FIG.28shows a computer system2801that is programmed or otherwise configured to direct the methods described herein and utilize the systems described herein. The computer system2801can regulate various aspects of the present disclosure, such as, for example, directing the cycles of probe binding described herein. The computer system2801can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

The computer system2801includes a central processing unit (CPU, also “processor” and “computer processor” herein)2805, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system2801also includes memory or memory location2810(e.g., random-access memory, read-only memory, flash memory), electronic storage unit2815(e.g., hard disk), communication interface2820(e.g., network adapter) for communicating with one or more other systems, and peripheral devices2825, such as cache, other memory, data storage and/or electronic display adapters. The memory2810, storage unit2815, interface2820and peripheral devices2825are in communication with the CPU2805through a communication bus (solid lines), such as a motherboard. The storage unit2815can be a data storage unit (or data repository) for storing data. The computer system2801can be operatively coupled to a computer network (“network”)2830with the aid of the communication interface2820. The network2830can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network2830in some cases is a telecommunication and/or data network. The network2830can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network2830, in some cases with the aid of the computer system2801, can implement a peer-to-peer network, which may enable devices coupled to the computer system2801to behave as a client or a server.

The CPU2805can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory2810. The instructions can be directed to the CPU2805, which can subsequently program or otherwise configure the CPU2805to implement methods of the present disclosure. Examples of operations performed by the CPU2805can include fetch, decode, execute, and writeback.

The CPU2805can be part of a circuit, such as an integrated circuit. One or more other components of the system2801can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

The storage unit2815can store files, such as drivers, libraries and saved programs. The storage unit2815can store user data, e.g., user preferences and user programs. The computer system2801in some cases can include one or more additional data storage units that are external to the computer system2801, such as located on a remote server that is in communication with the computer system2801through an intranet or the Internet.

The computer system2801can communicate with one or more remote computer systems through the network2830. For instance, the computer system2801can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC's (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system2801via the network2830.

Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system2801, such as, for example, on the memory2810or electronic storage unit2815. The machine executable or machine-readable code can be provided in the form of software. During use, the code can be executed by the processor2805. In some cases, the code can be retrieved from the storage unit2815and stored on the memory2810for ready access by the processor2805. In some situations, the electronic storage unit2815can be precluded, and machine-executable instructions are stored on memory2810.

The computer system2801can include or be in communication with an electronic display2835that comprises a user interface (UI)2840for providing, for example, the detectable signal sequences mentioned herein or the identification of analytes as mentioned herein or the location of analytes as disclosed herein or any other information disclosed herein. Examples of UI's include, without limitation, a graphical user interface (GUI) and web-based user interface.

Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit2805. The algorithm can, for example, direct the optical modules disclosed herein to capture an image or direct probe binding.

Equivalents and Scope

All cited sources, for example, references, publications, databases, database entries, and art cited herein, are incorporated into this application by reference, even if not expressly stated in the citation. In case of conflicting statements of a cited source and the instant application, the statement in the instant application shall control.

Section and table headings are not intended to be limiting.

Exemplary Embodiments

1. A method for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the method comprising:(a) providing a substrate comprising a surface, wherein the surface comprises said plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module, and wherein said surface comprises reagents for sequencing by synthesis;(b) performing a plurality of cycles of probe binding to said plurality of analytes, a cycle of said plurality of cycles comprising:(i) contacting said plurality of analytes with a plurality of probes, a probe of said plurality of probes comprising a detectable label;(c) identifying said detectable labels for a cycle of the plurality of cycles, wherein said identifying comprises applying a correction based on a neighbor effect and a relative position of one or more analytes of said plurality of analytes; and(d) identifying said analyte from said identified detectable labels across said plurality of cycles.
2. The method of embodiment 1, wherein said surface is patterned.
3. The method of embodiment 1, wherein said surface is unpatterned.
4. The method of embodiment 1, wherein said correction comprises use of a distance-dependent correction factor.
5. The method of embodiment 1, wherein said correction comprises use of a pattern-dependent correction factor.
6. The method of embodiment 5, wherein said use of the pattern-dependent correction factor comprises a determination of one or more relative positions of one or more analytes of said plurality of analytes and a determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes.
7. The method of embodiment 6, wherein said one or more relative positions of said analytes and said one or more distances relative to a number of pixels between said relative positions of said analytes are applied to a reference pixel grid to determine one or more interfering optical signals derived from one or more neighboring analytes.
8. The method of embodiment 6, wherein said one or more distances relative to a number of pixels between one or more pixels adjacent to a relative position of a first analyte of said plurality of analytes and one or more pixels adjacent to a relative position of a second analyte of said plurality of analytes to determine one or more interfering optical signals derived from one or more neighboring analytes.
9. The method of embodiment 6, wherein said determination of one or more relative positions of said analytes of said plurality of analytes and said determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes are applied to said neighboring effect of one or more adjacent analytes of said plurality of analytes to determine one or more interfering optical signals derived from said analyte, wherein said adjacent analytes are adjacent to said analytes of said plurality of analytes.
10. The method of any one of the preceding embodiments, wherein said relative position of said analyte of said plurality of analytes, said neighboring effect of an analyte of said plurality of analytes, or both are determined at least in part by use of a trained machine learning algorithm.
11. The method of embodiment 1, wherein said analytes are DNA concatemers.
12. The method of embodiment 11, wherein said DNA concatemers are hybridized to ssDNA hairs.
13. The method of embodiment 1, wherein said analytes are proteins or peptides.
14. The method of embodiment 1, wherein said probes comprise a plurality of reversible terminator nucleotides.
15. The method of embodiment 11, wherein said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label.
16. The method of embodiment 1, wherein said resolving comprises removing interfering optical signals from a neighboring polynucleotide using a center-to-center distance between said neighboring polynucleotides from said determined relative positions.
17. The method of embodiment 16, wherein said resolving function comprises deconvolution.
18. The method of embodiment 16, wherein said polynucleotides are packed on said substrate such that there is overlap between optical signals emitted by said detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein said adjacent polynucleotides each comprise a distinct sequence.
19. The method of embodiment 16, wherein the polynucleotides are deposited on said surface at an average density of more than 4 molecules per square micron.
20. The method of embodiment 1, wherein said relative position of said analytes deposited to the surface of the substrate is determined within 10 nm RMS.
21. A system for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the system comprising:(a) a substrate comprising a surface, wherein the surface comprises said plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module, and wherein said surface comprises reagents for sequencing by synthesis;(b) an optical imaging device configured to perform a plurality of cycles of probe binding to said plurality of analytes, wherein one or more cycles of said plurality of cycles comprises contacting said plurality of analytes with a plurality of probes, a probe of said plurality of probes comprising a detectable label;(c) an image processing module, said image processing module configured to:(i) identify said detectable labels a cycle of the plurality of cycles, wherein said identifying comprises applying a correction based on a neighbor effect and a relative position of one or more analytes of said plurality of analytes; and(ii) identify said analytes disposed on the surface of the substrate from said identified detectable labels across said plurality of cycles.
22. The system of embodiment 21, wherein said surface is patterned.
23. The system of embodiment 22, wherein said surface is unpatterned.
24. The system of embodiment 21, wherein said correction comprises use of a distance-dependent correction factor.
25. The system of embodiment 21, wherein said correction comprises use of a pattern-dependent correction factor.
26. The system of embodiment 25, wherein said use of the pattern-dependent correction factor comprises a determination of one or more relative positions of said analytes of said plurality of analytes and a determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes.
27. The system of embodiment 26, wherein said one or more relative positions of said analytes and said one or more distances relative to a number of pixels between said relative positions of said analytes are applied to a reference pixel grid to determine one or more interfering optical signals derived from one or more neighboring analytes.
28. The system of embodiment 26, wherein said one or more distances relative to a number of pixels between one or more pixels adjacent to a relative position of a first analyte of said plurality of analytes and one or more pixels adjacent to a relative position of a second analyte of said plurality of analytes to determine one or more interfering optical signals derived from one or more neighboring analytes.
29. The system of embodiment 26, wherein said determination of one or more relative positions of said analytes of said plurality of analytes and said determination one or more distances relative to a number of pixels between said relative positions of said analytes of said plurality of analytes are applied to said neighboring effect of an analyte of said plurality of analytes to determine one or more interfering optical signals derived from said analyte.
30. The system of any one of the preceding embodiments, wherein said relative position of said analyte of said plurality of analytes, said neighboring effect of an analyte of said plurality of analytes, or both are determined at least in part by use of a trained machine learning algorithm.
31. The system of embodiment 21, wherein said analytes are DNA concatemers.
32. The system of embodiment 31, wherein said DNA concatemers are hybridized to ssDNA hairs.
33. The system of embodiment 21, wherein said analytes are proteins or peptides.
34. The system of embodiment 21, wherein said probes comprise a plurality of reversible terminator nucleotides.
35. The system of embodiment 31, wherein said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label.
36. The system of embodiment 21, wherein said resolving comprises removing interfering optical signals from a neighboring polynucleotide using a center-to-center distance between said neighboring polynucleotides from said determined relative positions.
37. The system of embodiment 36, wherein said resolving function comprises deconvolution.
38. The system of embodiment 36, wherein said polynucleotides are packed on said substrate such that there is overlap between optical signals emitted by said detectable labels from nucleotides incorporated into adjacent polynucleotides, and wherein said adjacent polynucleotides each comprise a distinct sequence.
39. The system of embodiment 36, wherein the polynucleotides are deposited on said surface at an average density of more than 4 molecules per square micron.
40. The system of embodiment 21, wherein said relative position of said analytes deposited to the surface of the substrate is determined within 10 nm RMS.21.
41. A method for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the method comprising:(a) providing a substrate comprising a surface, wherein the surface comprises said plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module, and wherein said surface further comprises reagents for sequencing by synthesis; and(b) performing a plurality of cycles of probe binding to said plurality of analytes wherein a cycle of said plurality of cycles comprises contacting said plurality of analytes with a plurality of probes, a probe of said plurality of probes comprising a detectable label; and(c) cleaving one or more detectable labels by applying a cleaving solution.
42. The method of embodiment 41, wherein said surface is patterned.
43. The method of embodiment 42, wherein said surface is unpatterned.
44. The method of embodiment 41, wherein said analytes are DNA concatemers.
45. The method of embodiment 44, wherein said DNA concatemers are hybridized to ssDNA hairs.
46. The method of embodiment 41, wherein said analytes are proteins or peptides.
47. The method of embodiment 41, wherein said probes comprise a plurality of reversible terminator nucleotides.
48. The method of embodiment 47, wherein said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label.
49. The method of embodiment 49, wherein the cleaving solution comprises TCEP ((tris(2-carboxyethyl)phosphine) and THPP (Tris(hydroxypropyl)phosphine).
50. The method of embodiment 49, wherein the TCEP has a concentration of about 10 mM to about 150 mM.
51. The method of embodiment 50, wherein the TCEP has a concentration of about 150 mM.
52. The method of embodiment 49, wherein the THPP has a concentration of about 5 mM to about 100 mM.
53. The method of embodiment 51, wherein the TCEP has a concentration of about 150 mM and the THPP has a concentration of about 40 mM to about 50 mM.
54. A system for identifying an analyte of a plurality of analytes disposed on a surface of a substrate, the system comprising:(a) a substrate comprising a surface, wherein the surface comprises said plurality of analytes disposed on the surface at a density such that a minimum effective pitch between binding locations of analytes of said plurality of analytes is less than λ/(2*NA), wherein ‘NA’ is a numerical aperture of said optical imaging module, and wherein said surface comprises reagents for sequencing by synthesis;(b) an optical imaging device configured to perform a plurality of cycles of probe binding to said plurality of analytes, wherein one or more cycles of said plurality of cycles comprises contacting said plurality of analytes with a plurality of probes, a probe of said plurality of probes comprising a detectable label; and(c) a dispenser dispensing a cleaving solution to cleave the detectable label from the analyte.
55. The system of embodiment 54, wherein said surface is patterned.
56. The system of embodiment 54, wherein said surface is unpatterned.
57. The system of embodiment 54, wherein said analytes are DNA concatemers.
58. The system of embodiment 57, wherein said DNA concatemers are hybridized to ssDNA hairs.
59. The system of embodiment 54, wherein said analytes are proteins or peptides.
60. The system of embodiment 54, wherein said probes comprise a plurality of reversible terminator nucleotides.
61. The system of embodiment 60, wherein said plurality of reversible terminator nucleotides comprises at least four distinct nucleotides each with a distinct detectable label.
62. The system of embodiment 54, wherein said analytes are packed on said substrate such that there is overlap between optical signals emitted by said detectable labels from said probes bound to one or more adjacent analytes.
63. The system of embodiment 54, wherein said analytes are deposited on said surface at an average density of more than 4 analytes per square micron.
64. The system of embodiment 54, wherein the cleaving solution comprises TCEP ((tris(2-carboxyethyl)phosphine) and THPP (Tris(hydroxypropyl)phosphine).
65. The system of embodiment 64, wherein the TCEP has a concentration of about 10 mM to about 150 mM.
66. The system of embodiment 65, wherein the TCEP has a concentration of about 150 mM.
67. The system of embodiment 64, wherein the THPP has a concentration of about 5 mM to about 100 mM.
68. The system of embodiment 64, wherein the TCEP has a concentration of about 150 mM, and the THPP has a concentration of about 40 mM to about 50 mM.

Examples

The practice of the present disclosure may employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA techniques and pharmacology, within the skill of the art. Such techniques are explained fully in the literature. See, e.g., T. E. Creighton, Proteins: Structures and Molecular Properties (W.H. Freeman and Company, 1993); A. L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al., Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.); Remington's Pharmaceutical Sciences, 18th Edition (Easton, Pa.: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry 3rd Ed. (Plenum Press) Vols A and B(1992).

Example 1: Dense Packing of Molecules

Methods below will describe how to utilize a square ordered array where the pitch ranges between 200 nm and 333 nm. Additional methods will be described that allow even smaller pitches. An imaging system is described in International Application PCT/US2018/020737, filed Mar. 2, 2018 and incorporated herein by reference, which will be used as a reference system which enables sub-diffraction limit imaging. The optical system can include multiple 2,048 by 2,048 pixel cameras operating up to 100 Hz frames per second (fps) with field size 332.8 um by 332.8 um. This system is capable of measuring as little as a single fluor at and above 90 fps. Using this system with 1-10 copies (or 1-10 fluorophores) per molecule at 85 fps achieves the throughput to image a 63 mm×63 mm slide in under 15 minutes. Biochemistry cycles and imaging are continuously and simultaneously performed, either by using two chips or by dividing a single chip into at least 2 regions.

Example 2: Single-Molecule Sequencing Using Sequencing by Synthesis

Single-molecule sequencing using sequencing by synthesis approach was evaluated on the Apton System. To test the methodology, single-stranded DNA templates with 5′ phosphate group were first attached to the chip with a carbohydrazide activated silicon surface of the flow cell through EDC (1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide) chemistry. The sequencing primer was the annealed the target deposited on the surface. The sequencing templates used in our initial studies included synthetic oligonucleotide containing EGFR L858R, EGFR T790M, and BRAF V600E mutations and two cDNA samples reversed transcribed from ERCC 00013 and ERCC 00171 control RNA transcripts. After DNA template immobilization and primer annealing, the flow cell is loaded on the Apton instrument for sequencing reactions, which involves multiple cycles of enzymatic single nucleotide incorporation reaction, imaging to detect fluorescence dye detection, followed by chemical cleavage. Therminator IX DNA Polymerase from NEB was used for single base extension reaction, which is a 9° NTM DNA Polymerase variant with an enhanced ability to incorporate modified dideoxy nucleotides. Four dNTPs used in the reaction are labeled with 4 different cleavable fluorescent dyes and blocked at 3′ —OH group with a cleavable moiety (dCTP-AF488, dATP-AFCy3, dTTP-TexRed, and dGTP-Cy5 from MyChem). During each sequencing reaction cycle, a single labeled dNTP is incorporated, and the reaction is terminated because of the 3′-blocking group on dNTP. After dNTP incorporation, the unincorporated nucleotides are removed from the flow-cell by washing and the incorporated fluorescent dye labeled nucleotide is imaged to identify the base. After the images are captured, the fluorescent dye and blocking moiety are cleaved from the incorporated nucleotide using 150 mM TCEP ((tris(2-carboxyethyl)phosphine)+40-50 mM THPP (Tris(hydroxypropyl)phosphine) at, pH9.0), allowing subsequent addition of the next complementary nucleotide in next cycle. This extension, detection and cleavage cycle is then repeated to increase the read length.

FIG.15Ashows results of sequencing of a 1:1 mixture of synthetic oligonucleotide templates corresponding to the region around codon 790 in the EGFR gene containing equal amounts of mutant and wild type (WT) targets. Images from incorporation of dye labeled nucleotides used to sequence synthetic templates corresponding to a region of the EGFR gene near codon 790 with a mutation at the first base (C-incorporation in WT & T-incorporation in mutant) after the primer. The montage inFIG.15Adepicts images from alternating base incorporation and cleavage cycles. This data exhibits the ability of the system to detect 10 cycles of base incorporation. Arrows indicate the base change observed.

The synthetic oligonucleotides used were around 60 nucleotides long. A primer that had a sequence ending one base prior to the mutation in codon 790 was used to enable the extension n reaction. The surface was imaged post incorporation of nucleotides by the DNA polymerase and after the cleavage reaction with TCEP. The yellow circle indicates the location of the template molecule that was aligned using data from 10 consecutive cycles of dye incorporation. Molecules were identified with known color incorporation sequences, following that the actual base incorporations are identified by visual inspections which is labor—intensive.

Dye labeled nucleotides were used to sequence cDNA generated from RNA templates. RNA used was generated by T7 transcription from cloned ERCC control plasmids.FIG.15Bdepicts images from alternating base incorporation and cleavage cycles. The data exhibits the ability of the system to detect 10 cycles of base incorporation. The sequence observed were correct. Yellow arrows indicate the cleavage cycles.

Specifically, cDNA templates corresponding to transcripts generated from the ERCC (External RNA Controls Consortium) control plasmids by T7 transcription were sequenced. The cDNA molecule generated were >350 nucleotides long. The surface was imaged post incorporation of nucleotides by the DNA polymerase and after the cleavage reaction with TCEP. The yellow circle inFIG.15Bindicates the location of the template molecule that was aligned using data from 10 consecutive cycles of dye incorporation. Data indicated ability to manually detect 10 cycles of nucleotide incorporation by manual viewing of images

Example 3: Relative Location Determination for Analyte Variants

FIG.16is an image of single molecules deposited on a substrate and bound by a probe comprising a fluorophore. The molecules are anti-ERK antibodies bound to ERK protein from cell lysate which has been covalently attached to the solid support. The antibodies are labeled with 3-5 fluorophores per molecule. Similar images are attainable with single fluorescent nucleic acid targets, e.g., during sequencing by synthesis.

To improve accuracy of detection, the molecules undergo successive cycles of probe binding and stripping, in this case 30 cycles. In each round, the image is processed to determine the location of the molecules. The images are background subtracted, oversampled by 2×, after which peaks are identified. Multiple layers of cycles are overlaid on a 20 nm grid. The location variance is the standard deviation, or the radius divided by the square root of the number of measurements.FIG.17, right panel, shows each peak from each cycle overlaid. The left panel is the smoothed version of the right panel. Each bright spot represents a molecule. The molecule locations are resolvable with molecule-to-molecule distances under 200 nm.FIG.18shows localization variation for each of a plurality of molecules found in a field. The median localization variance is 5 nm and the 3-sigma localization variance is under 10 nm.

Example 4: Densely-Packed Sequencing Substrates and Single-Sided Density Single-Stranded Circle Formation

To prepare a library of concatemers comprising target sequences to distribute on the surface of a substrate in a randomly distributed close-packed layer, a sample comprising target sequences was amplified, purified, ligated to form circularized DNA, and quantified, as shown inFIG.23A.

Amplification of Targets

An Illumina MiSeq library was purchased from SegMatic (Fremont, Calif.) made with the standard protocol usingE. coliDNA purchased from Affymetrix (Santa Clara, Calif.—PN 14380)

The library was amplified by PCR amplification. Each PCR reaction included the following components listed in Table 1:

The primer mix is a 50:50 mix of P5-Phosphate (/5Phos/AAT GAT ACG GCG ACC ACC GA) and P7 (CAA GCA GAA GAC GGC ATA CGA GAT) primers at 10 uM:

The PCR amplification was performed under the following conditions: 5 mM at 94° C. followed by 35 cycles of: 94° C., 15 sec; 55° C., 30 sec; and 68° C., 30 sec. An aliquot of the amplification product was run on a 2% gel to verify the library molecule size (300-500 base pairs in this instance). The PCR amplification product was then purified using a PureLink® Spin Column (Thermofisher) according to the manufacturer's protocol.

Circularization of Target DNA

The purified PCR amplification products were then subject to single strand circularization by ligation in the reaction mix described in Table 2:

The bridging oligonucleotide sequence was TCG GTG GTC GCC GTA TCA TTC AAG CAG AAG ACG GCA TAC GAG AT.

After ligation, 14 each of Exonuclease I and Exonuclease III (New England Biolabs) were added and the reaction is incubated for an additional 45 min at 37° C. and 30 min at 85° C. The resulting material was purified using a Zymo-Spin™ Column (Oligo Clean & Concentrators kit Zymo Research, Irvine, Calif.) using the manufacturer's protocol. After purification, the concentration was measured using a Qubit 2.0 fluorometer (ThermoFisher) and Quant-iT OliGreen® (ThermoFisher) with custom calibration samples using an oligonucleotide of known concentration.

Concatemer Formation from Circularized DNA

Concatemers from circularized DNA comprising the the target sequence were formed in a reaction mix described in Table 3:

The circular template+primer mix was incubated for 10 mM at 90° C., and then 30 min at 30° C. A pre-warmed enzyme mix was then added as in Table 3 for 90 mM. The reaction was stopped with the addition of reaction inactivation buffer and stored at 4° C.

Concatemer libraries were then layered on a substrate to form a densely-packed, randomly distributed layer bound to the surface of a substrate, followed by sequencing the bound concatemers via imaging and image processing, and analysis of the data, as shown inFIG.23Band as described below.

One microliter of the sequencing substrate was mixed with 19 ul of citrate phosphate buffer, and 10 ul was loaded onto a custom biochip and incubated overnight. The chip was then washed 2× with citrate phosphate buffer, 2× with potassium phosphate buffer and 2× with NA wash 3 buffer.

Fluorescent probe was bound to the concatemer layer bound to the surface of the chip to determine identity. Images showing the density are shown inFIGS.25A-25C.FIG.25Dshows a plot of measured density of a 1-sided concatemer layer according to methods described herein (Apton—control target) and simulated distributions at higher densities (Apton—Sim).

Sequencing by synthesis was performed using standard sequencing chemistries. The chip comprising the densely packed concatemer layer was loaded into the AptonBio Sequencer and washed 6×5 mM at 60° C. with Wash1 (20 mM Tris-HCl, 10 mM (NH4)2 SO4, 10 mM KCl, 2 mM MgSo4, 0.1% 100, pH 8.8 @ 25° C., 50 mM NaCl). The sequencing oligo (ATC TCG TAT GCC GTC TTC TGC TTG) was diluted to 100 nM in hybridization buffer and incubated 1×1 mM followed by 2×10 mM at 60° C. with Wash1 washes between hybridization operations. Then thirty-two cycles of the following 8 operations were performed:

1—Cleavage: 225 sec at 60° C. with buffer in Table 4

5—Extension: 450 sec at 60° C. with buffer in Table 5

Reads of 30-40 bp are shown inFIG.27A. Reads of 20-25 bp are shown inFIG.27B.

Cross-plots shown inFIG.27Cshow the resolution of base calling at individual spots forE. colisequencing.

Example 6: Nucleotide Detection Using an Erythorbic Buffer Solution

Incorporated nucleotides from two 51-cycle sequencing runs of a set of 69 or 70 fields were imaged and the density of high-quality mapped human reads of 40 or 50 bases plotted in reads per square micron. Two run image sets were imaged: one in Wash2GE buffer (with erythorbic acid), and one in Wash2GE buffer (without erythorbic acid). The Wash2GE buffer with erythorbic acid was comprised of 20 millimolar (mM) Tris-HCl, 10 mM Glutathione, and 5 mM Erythorbic Acid at pH 8, while the Wash2GE buffer without Erythorbic acid was comprised of 20 mM Tris-HCl and 10 mM Glutathione with a pH of 8.8.

The density readings shown inFIG.29show that, at both 40 and 50 bases, there was a lower mapped density without erythorbic acid. Additionally,FIG.29also shows the rate of mapping did not decrease as rapidly when imaged with erythorbic acid. That is, the difference in mapping density was similar between 40 and 50 base reads with erythorbic acid, whereas the distributions without erythorbic acid overlapped only with the lower mapping outliers of the 40 base reads. These data indicate that imaging in the presence of erythorbic acid enables both higher read density and longer reads.

Example 7: Determination of Super-Resolved Molecule Location

Target nucleic acid molecules were processed by the methods described herein and dispensed onto an unpatterned substrate. During a first cycle of probe-binding, probes comprising fluorophores were dispensed onto the substrate and images of the substrate were taken. A ratio of the first to second molecule fluorescence intensities (i.e. molecule map) is used to determine super-resolved molecule location (FIG.37). The location of each target nucleic acid molecule is determined where the signal-to-noise ratio (SNR) of the molecule on the substrate is at its best. SNR is calculated herein as (I1−I2)/I1 I1=brightest. Regions with high SNR are lightly colored and wherein regions with a low SNR are dark in color. At each molecule center, the SNR was determined with a calculated confidence (FIG.38A), the SNR was non-linearly filtered (FIG.38B), and the base color was called for each pixel (FIG.38C). The color was called for one molecule, molecule464, over 4 cycles.

The color intensity recorded for molecule464for each cycle was compared to determine the true color call. Molecules58and196were located adjacent to molecule464and on opposing sides. Molecules58and196both called the same color, red. The color intensity of molecules58and196interfered with the color intensity of molecule464. To resolve this, the color intensity recorded for molecule464for each cycle was compared over 4 cycles (FIGS.41A-B). As seen inFIGS.41A-B, the vertical black line on the left-hand side of each panel indicates molecule464center position and the gray vertical line on the right of each panel indicates the neighbor position. In cycle 1 molecule464calls green as the green intensity is clearly higher at464than any other color. In cycle 4, however, molecule464calls red, but with low confidence, as the red and green intensities are almost equal. The line profiles show that the real peak at464is green, but that the neighboring intensities of molecules196and58(with red peaks at the gray line) interfere with the calling the correct color. Thus, it was determined that the color call for molecule464was green.