Patent Description:
Generally, there is a need to increasingly automate biological analysis systems to increase efficiency. For example, advances in automated biological sample processing instruments allow for quicker and more efficient analysis of samples. There is also an increasing need to provide biological analysis systems with designs that cater to user needs, such as ease of install, ease of use, minimal necessary lab space.

Instruments that analyze dye-labeled samples generally analyze the results of sample runs. However, problems with run conditions that might make the resulting data not reliable are generally not identified, or are identified so late that samples and time are wasted, thereby lengthening the research process. Such instruments use a dye matrix to correlate incoming spectral data with the particular dyes usable with the instrument. The dye matrix identifies normalized expected values for each dye usable with the system. Existing instruments typically require that the normal runtime operation of the system, in which samples of interest are processed in the instrument, be supplemented by and/or interrupted to carry out a special calibration process that is typically performed by the end user. For example, such a process might require a special "calibration run" in which known dyes are run through the system and the resulting spectral data is used to calibrate or re-calibrate the dye matrix used by the system.

In addition, in capillary electrophoresis, it takes extra time and money for an operator to control the signal levels of their samples. They must control their signal levels in order to get accurate signals that have a good signal-to-noise, without saturating the detector, i.e. the camera. When the camera is saturated, with a conventional data analysis workflow, the dye signals that are measured are inaccurate. The main peak is distorted, and false peaks called "pull-up" are artificially created. One way the operator can control their signal levels is by quantitating their input biological sample such as, e.g., input DNA. The operator can measure the concentration of input DNA, then dilute said concentration if it is too high, both of which take extra time and add cost.

While a user can manually interpret data that is off-scale, this takes extra time. In their data analysis workflow, peaks that have saturated the camera are called "off-scale", and are flagged. Their data analysis workflow requires the user to visually inspect all flagged peaks.

There is therefore a need for an automated recovery of off-scale data that bypasses the additional steps of visual inspection of off-scale peaks and quantitating of input DNA.

<CIT> discloses a method for quality control of a process performed on a sample, the method comprising: providing a plurality of capillaries; providing a polymer solution source; applying a pressure to the polymer solution source and transferring at least a portion of polymer solution into the capillaries; while applying the pressure, measuring at least one parameter indicative of a sensed value of pressure over time to obtain pressure values; performing an analysis on at least a portion of the pressure values; and performing an action based on the analysis. To remove spectral error can involve using optimization techniques to modify the elements of the instrument's dye matrix so that the spectral error or an appropriate proxy for the spectral error is minimized. The dye matrix is a matrix with the normalized dye spectra of each dye as its rows and spectral bin number as its columns.

A biological analysis device, an associated method, and a non-transitory computer-readable storage medium are provided. The method comprises the features of claim <NUM>. Preferred embodiments of the method are defined in dependent claims <NUM> - <NUM>. The non-transitory computer-readable storage medium comprises the features of claim <NUM>. Preferred embodiments of the storage medium are defined in dependent claims <NUM> - <NUM>. The biological analysis device comprises the features of claim <NUM>. Preferred embodiments of the device are defined in dependent claims <NUM> - <NUM>.

Additional aspects, features, and advantages of the present invention are set forth in the following description and claims, particularly when considered in conjunction with the accompanying drawings in which like parts bear like reference numbers.

The following description provides embodiments of the present invention, which are generally directed to systems, devices, and methods for preparing, observing, testing, and/or analyzing biological samples. Such description is not intended to limit the scope of the present invention, but merely to provide a description of embodiments.

<FIG> depicts a schematic view of a biological analysis device <NUM> according to an exemplary embodiment of the disclosure. The biological analysis device <NUM> is configured to perform capillary electrophoresis and includes a cartridge <NUM> that is configured to be easily replaceable by a user (e.g., operator or other personnel) of the biological analysis device <NUM>. The cartridge <NUM> combines various elements of the biological analysis device within a multi-function, integrated, easily replaceable unit. For example, the cartridge <NUM> includes one or more capillaries <NUM> (only one depicted in <FIG>), one or more cathodes <NUM> coupled with a cathode end of the one or more capillaries <NUM>, and a fluidics section <NUM>. The cartridge <NUM> also includes a detection section <NUM> including various components configured to interface with an optical detection system (not shown) of the biological analysis device <NUM>.

The fluidics section <NUM> includes one or more storage devices (e.g., reservoirs, containers) that contain a separation medium (e.g., a polymer gel) and a buffer. In the exemplary embodiment of <FIG>, the fluidics section <NUM> includes a buffer reservoir <NUM> and a separation medium container <NUM>. The fluidics section <NUM> further includes a manifold <NUM> configured to fluidically couple the buffer reservoir <NUM> and the separation medium container <NUM> with an anode end of the one or more capillaries <NUM>. The manifold <NUM> may include one or more valves and one or more fluid transfer devices, for example.

The biological analysis device <NUM> includes an actuation section <NUM> configured to interface with the fluidics section <NUM>. For example, the actuation section <NUM> may be configured to actuate one or more fluid control devices, such as one or more valves and/or fluid transfer devices of the fluidics section <NUM>.

The biological analysis device <NUM> includes a voltage section <NUM> configured to generate a voltage potential between the cathode <NUM> and an anode <NUM> that is electrically coupled with a buffer contained in the buffer reservoir <NUM>. In use, the one or more capillaries are filled with the polymer separation medium, and an electrically conductive fluid connection is established between the one or more capillaries <NUM> and the anode <NUM> through the buffer. A voltage differential is applied between the cathode <NUM>, which is also submerged in a buffer, and the anode <NUM>. As one having ordinary skill in the art would be familiar with, the voltage differential causes charged analytes to migrate through the one or more capillaries <NUM>, which are filled with the separation medium, where the analytes separate and are detected in the detection section <NUM> using the optical detector device of the biological analysis device <NUM>.

The biological analysis device <NUM> further includes a temperature regulation section <NUM> that regulates the temperature of the one or more capillaries <NUM>. The temperature regulation section <NUM> is configured to mate with the cartridge <NUM> and includes a heating element <NUM>, a temperature sensor (e.g., a thermistor) <NUM>, and an air movement device (not shown) that generates a flow of warmed air <NUM> through the cartridge <NUM> to maintain the temperature of the one or more capillaries <NUM> at a desired value.

The components associated with the user-replaceable cartridge <NUM> may be housed in a cartridge housing, and the cartridge housing may include one or more features configured to interface with features of the biological analysis device <NUM>. For example, various features of the cartridge <NUM> may interface with features of the biological analysis device <NUM> to ensure correct positioning and alignment of the cartridge <NUM>, and its associated components, and to enable the biological analysis device <NUM> to actuate components of the fluidics section <NUM>. Further interfacing features enable the cartridge <NUM> to interface with the temperature regulation section <NUM> and the voltage section <NUM>.

The interface between the cartridge <NUM> and the biological analysis device <NUM>, and the particular division of functional components between the cartridge <NUM> and the biological analysis device <NUM> may be configured and selected to facilitate use and reliable operation of the biological analysis device. For example, the configuration of the biological analysis device <NUM> and cartridge <NUM> is chosen to mitigate, if not eliminate, failure modes due to user error.

Methods in accordance with embodiments described herein, are implemented in a computer system.

Those skilled in the art will recognize that the operations of the various embodiments may be implemented using hardware, software, firmware, or combinations thereof, as appropriate. For example, some processes can be carried out using processors or other digital circuitry under the control of software, firmware, or hard-wired logic. (The term "logic" herein refers to fixed hardware, programmable logic and/or an appropriate combination thereof, as would be recognized by one skilled in the art to carry out the recited functions. ) Software and firmware can be stored on non-transitory computer-readable media. Some other processes can be implemented using analog circuitry, as is well known to one of ordinary skill in the art.

<FIG> is a block diagram that illustrates a computer system <NUM> that may be employed to carry out processing functionality, according to various embodiments. Instruments to perform experiments may be connected to the exemplary computing system <NUM>. According to various embodiments, the instruments that may be utilized include, for example, the biological analysis device <NUM> of <FIG>. Computing system <NUM> includes one or more processors, such as a processor <NUM>. Processor <NUM> can be implemented using a general or special purpose processing engine such as, for example, a microprocessor, controller or other control logic. Processor <NUM> can be connected to a bus <NUM> or other communication medium.

Referring to <FIG>, a computer system <NUM> may provide control to the function of biological analysis device <NUM> in <FIG>, as well as the user interface function. Additionally, computer system <NUM> of <FIG> may provide data processing, display and report preparation functions. All such instrument control functions may be dedicated locally to the biological analysis device. As such, computer system <NUM> can serve as control system to biological analysis device <NUM>. Computer system <NUM> of <FIG> may also provide remote control of part or all of the control, analysis, and reporting functions.

Computing system <NUM> of <FIG> may also be embodied in any of a number of forms, such as a rack-mounted computer, mainframe, supercomputer, server, client, a desktop computer, a laptop computer, a tablet computer, hand-held computing device (e.g., PDA, cell phone, smart phone, palmtop, etc.), cluster grid, netbook, embedded systems, or any other type of special or general purpose computing device as may be desirable or appropriate for a given application or environment. Additionally, a computing system <NUM> can include a conventional network system including a client/server environment and one or more database servers, or integration with LIS/LIMS infrastructure. A number of conventional network systems, including a local area network (LAN) or a wide area network (WAN), and including wireless and/or wired components, are known in the art. Additionally, client/server environments, database servers, and networks are well documented in the art. According to various embodiments described herein, computing system <NUM> may be configured to connect to one or more servers in a distributed network. Computing system <NUM> may receive information or updates from the distributed network. Computing system <NUM> may also transmit information to be stored within the distributed network that may be accessed by other clients connected to the distributed network.

Computing system <NUM> of <FIG> also includes a memory <NUM>, which can be a random access memory (RAM) or other dynamic memory, coupled to bus <NUM> for storing instructions to be executed by processor <NUM>. Memory <NUM> also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor <NUM>.

Computing system <NUM> further includes a read only memory (ROM) <NUM> or other static storage device coupled to bus <NUM> for storing static information and instructions for processor <NUM>.

Computing system <NUM> may also include a storage device <NUM>, such as a magnetic disk, optical disk, or solid state drive (SSD) is provided and coupled to bus <NUM> for storing information and instructions. Storage device <NUM> may include a media drive and a removable storage interface. A media drive may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), flash drive, or other removable or fixed media drive. As these examples illustrate, the storage media may include a computer-readable storage medium having particular computer software, instructions, or data stored therein.

In alternative embodiments, storage device <NUM> may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing system <NUM>. Such instrumentalities may include, for example, a removable storage unit and an interface, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the storage device <NUM> to computing system <NUM>.

Computing system <NUM> of <FIG> can also include a communications interface <NUM>. Communications interface <NUM> can be used to allow software and data to be transferred between computing system <NUM> and external devices. Examples of communications interface <NUM> can include a modem, a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a RS-232C serial port), a PCMCIA slot and card, Bluetooth, etc. Software and data transferred via communications interface <NUM> are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communications interface <NUM>. These signals may be transmitted and received by communications interface <NUM> via a channel such as a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of a channel include a phone line, a cellular phone link, an RF link, a network interface, a local or wide area network, and other communications channels.

Computing system <NUM> may be coupled via bus <NUM> to a display <NUM>, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device <NUM>, including alphanumeric and other keys, is coupled to bus <NUM> for communicating information and command selections to processor <NUM>, for example. An input device may also be a display, such as an LCD display, configured with touchscreen input capabilities. Another type of user input device is cursor control <NUM>, such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor <NUM> and for controlling cursor movement on display <NUM>. A computing system <NUM> provides data processing and provides a level of confidence for such data. Consistent with certain implementations of embodiments of the present teachings, data processing and confidence values are provided by computing system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in memory <NUM>. Such instructions may be read into memory <NUM> from another computer-readable medium, such as storage device <NUM>. Execution of the sequences of instructions contained in memory <NUM> causes processor <NUM> to perform the process states described herein. Alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to implement embodiments of the present teachings. Thus, implementations of embodiments of the present teachings are not limited to any specific combination of hardware circuitry and software.

The term "computer-readable medium" and "computer program product" as used herein generally refers to any media that is involved in providing one or more sequences or one or more instructions to processor <NUM> for execution. Such instructions, generally referred to as "computer program code" (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system <NUM> to perform features or functions of embodiments of the present invention. These and other forms of non-transitory computer-readable media may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, solid state, optical or magnetic disks, such as storage device <NUM>. Volatile media includes dynamic memory, such as memory <NUM>. Transmission media includes coaxial cables, copper wire, and fiber optics, including the wires that comprise bus <NUM>.

Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor <NUM> for execution. For example, the instructions may initially be carried on magnetic disk of a remote computer. A modem local to computing system <NUM> can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector coupled to bus <NUM> can receive the data carried in the infra-red signal and place the data on bus <NUM>. Bus <NUM> carries the data to memory <NUM>, from which processor <NUM> retrieves and executes the instructions. The instructions received by memory <NUM> may optionally be stored on storage device <NUM> either before or after execution by processor <NUM>.

However, it will be apparent that any suitable distribution of functionality between different functional units, processors or domains may be used without detracting from the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Hence, references to specific functional units are only to be seen as references to suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization.

<FIG> depicts an example of a <NUM>-capillary subset of a raw full frame image provided by a CCD camera of a biological analysis device. In this example, each capillary uses the data from three rows of the raw image along the vertical axis, measured in pixels. The horizontal axis corresponds to the wavelength of the detected signal, also measured in pixels.

The bright spots <NUM> correspond to the Raman signal coming from the polymer alone, while the fainter spots <NUM> to the left correspond to the biological sample. The raw spectral data is typically binned into <NUM> bins on the CCD before it is sent to the computing system for analysis.

The full frame of a CCD camera has usually too many columns of data to be read out in a timely manner, so multiple columns are combined into spectral "bins", each bin combining the electrons from one or more camera pixels. Bins can have uneven widths, which is referred to as "variable binning", to balance out the signals between different dyes.

The electrons from the CCD are thus combined together before they are read into a digital number between <NUM> and <NUM>. However, if there are too many electrons and their combined number is converted to a signal that exceeds the <NUM> limit, the resulting measured signal will not be accurate. Such signal will be called "saturated" or "off-scale". One can therefore see that the binning can take data that was on-scale in the full image and make it off-scale because the combined signal in a bin is greater than <NUM>.

One possible way to decrease the occurrence of off-scale data is to use narrower bins, thereby combining fewer of the original pixels in each bin. But narrowing the bins increases their number and therefore the time it takes to read a complete image.

<FIG> shows an example of a high sample concentration making the data slightly off scale. Data that is off scale has distorted peaks in the main dye and false peaks in other dyes. The slightly off-scale data is recoverable with visual inspection, but requires operator intervention.

<FIG> shows an example of a high sample concentration making the data severely off scale. This kind of severely off-scale data is not recoverable and necessitates a re-run with different parameters.

<FIG> shows a preliminary comparison between results obtained with a conventional method (lower plot - "Original Data Analysis") and a method in accordance with the present teachings (upper plot - "With Extended Dynamic Range Analysis applied").

A "Dye Matrix" is a response matrix used to recover dye data from the bin data. Each component in the dye matrix is the expected signal in each bin for one unit of each dye. <FIG> depicts a graphical representation of a dye matrix for four dyes over twenty bins. Each point represents the signal obtained in each bin for a unit of each dye. For instance, the left-most dye (represented by diamonds) reaches its maximum signal (<NUM>) in bin number <NUM>, while the next dye (represented by squares) reaches its maximum in bin number <NUM>. The dye matrix can also be represented by its coefficients, as shown in Table <NUM>, where each row corresponds to a different dye:.

To recover the dye data from the bin data, one just needs to multiply the bin data vector by the pseudoinverse matrix of the dye matrix, i.e. DM+ shown in Table <NUM>: <MAT>.

An interesting and unexpected property of the dye matrix is that, for a given dye, one will get the same reconstructed dye signal no matter which bin is used, provided that the proper component of the matrix is used to calculate the signal. This is illustrated in <FIG>, which show a comparison between reconstructed dye signals obtained by using, respectively, a full dye matrix (<FIG>) and a modified dye matrix (<FIG>). In this example, the full dye matrix corresponds to a response matrix for <NUM> dyes over <NUM> bins. In the modified dye matrix, the coefficients for bins <NUM>, <NUM>, <NUM> and <NUM> have been set to zero. The reconstructed dye signal obtained using the modified dye matrix (<FIG>) shows no significant difference when compared to the dye signal obtained with the full matrix (<FIG>).

This property allows the recovery of off-scale peaks. <FIG> shows an example of a saturated peak signal for one scan plotted as a function of bin number. In this example, <NUM> bins have off-scale data, which means that the number of accumulated electrons exceeded the maximum number available to count them (typically, <NUM>).

In one embodiment, the proposed recovery method, as shown in <FIG> and <FIG>, starts with the identification (<NUM>) and flagging (<NUM>) of bins in which the count exceeds a maximum number of counts. The image is then processed (<NUM>) taking these flags into account.

As a way to further improve the process, the binning pattern on the camera can be changed (<NUM>) prior to image acquisition. This change can be made by physically changing the camera hardware. It can also be accomplished by configuring the camera through modification of its firmware, and it can also be made dynamically by setting parameters in the image acquisition software.

The processing of an image, in accordance with an embodiment of the present teachings is further described in <FIG>. In a first step <NUM>, any coefficient of the dye matrix, corresponding to a bin that has been flagged as off-scale, is set to zero, thereby creating a modified dye matrix. Then, at step <NUM>, a modified pseudoinverse dye matrix is calculated using the modified dye matrix. A recovered dye signal is finally computed at step <NUM>, using the modified pseudoinverse dye matrix.

<FIG> shows the results obtained with the method described above. The upper plot shows uncorrected data, in which off-scale peaks produce pull-up and pull-down peaks. The lower plot shows corrected results, in which the elimination of off-scale bins yields recovered dye signals for each dye. In particular, the off-scale peaks recover their true height, and the artificial pull-up and pull-down peaks are eliminated.

Claim 1:
A computer-implemented method for recovering off scale data in an image produced by a camera in a capillary electrophoresis instrument (<NUM>), wherein multiple columns of raw spectral data of a full frame of the camera are combined into spectral bins, each bin combining electrons from one or more camera pixels, the method comprising:
identifying (<NUM>) bins of the image where electron counts produce a signal greater than a maximum camera signal;
setting (<NUM>) an off-scale flag for the identified bins; and
processing (<NUM>) the image to obtain a recovered dye signal, based on the flag set for each bin, and using a dye matrix, wherein processing the image further characterized by:
setting (<NUM>) to zero any coefficient of the dye matrix corresponding to bins that have been flagged as off-scale;
calculating (<NUM>) a modified pseudoinverse dye matrix; and
calculating (<NUM>) the recovered dye signal using the modified pseudoinverse dye matrix.