Patent Description:
The present disclosure relates to particular particle processing systems and methods. More particularly, the present disclosure relates to systems and methods for monitoring operational characteristics of a particle sorting system.

In the fields of biology and medicine, there is often a need for high throughput analysis and sorting of particles. One well known technique for sorting particles is droplet deflection. See, for example, <CIT>. In droplet deflection a stream of suspended particles is broken into individual droplets, for example, using a piezoelectric mechanism. At the point of droplet formation, an electrical charging element is used to selectively charge each droplet. The charged droplet then free falls through an electrostatic field which deflects the charged particle into one of a plurality of receiving containers.

Another technique for deflecting particles involves utilizing switching or pressure mechanisms to divert a volume of fluid containing a particle into a selected branch channel of a flow-path defined on a microfluidic chip. See, for example, <CIT>.

<CIT> relates to a method for calibrating a particle flow sorting device, said device comprising an input channel and at least two output channels, a particle sorting means and at least one means for detecting particles arranged in an outlet channel, said method comprising the step of adjusting at least one of the main operating parameters of the sorting means among the deflection force that can be applied by the sorting means to said particle. <CIT> also relates to a method for correcting the operating parameters of a particle flow sorting device during its operation, comprising the steps of: verifying the efficiency of sorting, modifying at least one operating parameter until at least one effective sorting.

<CIT> relates to methods and an apparatus for a microfabricated fluorescence activated cell sorter based on an optical switch for control of cell routing through a microfluidic channel network. <CIT> includes packaging of the microfluidic channel network in a self-contained plastic cartridge that enables microfluidic channel network to macro-scale instrument interconnect, in a sterile, disposable format.

<CIT> relates to a sorting in which particles suspended in a fluid are conducted in a closed duct and pass a measurement location in which particles to be selected trigger a signal by a sensing device. At a downstream fork, a pressure wave generated in response to the signal diverts the stream containing the particles from one branch to the other. An apparatus for carrying out this method is disclosed and includes a supply duct for the particle stream, a measurement duct, a measurement position in the measurement duct, a fork downstream of the measurement duct leading to a sorting branch duct and waste branch duct, and a pressure wave generator which is disposed in one of the ducts leading away from the fork.

<CIT> relates to microfluidic devices, systems and techniques in connection with particle sorting in liquid, including cytometry devices and techniques and applications in chemical or biological testing and diagnostic measurements.

<CIT> relates to a sorting device for biological cells or viruses.

The invention is related to systems and methods as defined in the claims.

Particle processing systems and methods are disclosed herein which utilize a sort monitoring system to monitor an operational characteristic for a particle sorting system, for example a microfluidic particle sorting system. The operational characteristic for the particle sorting system is a characteristic related to the performance and operation of the particle sorting system. More particularly, the operational characteristic may be related to the performance and operation of a sorter or a group of sorters in the particle sorting system. In some embodiments, the operational characteristic may be monitored based on monitoring an output of a sorter or a group of sorters in the particle sorting system and, more particularly, may be monitored based on detecting particles or an absence of particles from an output of a sorter or a group of sorters.

Examples of operational characteristics which may be monitored according to the systems and methods of the present disclosure include but are not limited to particle count, particle type, sort error, sort fraction, yield, purity, recovery percentage, enrichment percentage, or the like. The sort monitoring system may further be configured to evaluate the monitored operational characteristic, for example, as related to sort performance, and, in some embodiments, take an action, for example, a notification action or a corrective or proactive action, based on the evaluation of the operational characteristic.

The sort modules and sorting systems of the present disclosure may be characterized as flow sorters and may generally be structurally and functionally distinguished from drop sorters, as discussed herein. The sort modules and sorting systems of the present disclosure preferably utilize microfluidics and comprise a closed-channel system for sorting particles. Microfluidic particle sorting technology takes advantages of a closed, sterile, and scalable approach to efficiently and/or quickly sort large numbers of particles. To this end, a plurality of sort modules may be combined, for example, on a single microfluidic chip substrate. Sensing and sorting functionalities may further interface with the chip or be included thereon.

The terms flow-channel and flow-path as used herein refer to a pathway formed in or through a medium that allows for movement of fluids, such as liquids and gases. Typical flow-channels in a microfluidic system have cross-sectional dimensions between about <NUM> and about <NUM>. In some embodiments, flow-channels have cross-sectional dimensions between about <NUM> and about <NUM>. In further embodiments, flow-channels have cross-sectional dimensions between about <NUM> and about <NUM>. One of ordinary skill in the art will be able to determine appropriate channel dimensions, for example, cross-sectional dimension, length, volume, or the like, of a flow-channel. A flow-channel can have any selected shape or arrangement. Examples of possible flow-channel cross-sectional geometries may include but are not limited to a linear or non-linear configuration, a U-shaped configuration, a V-shaped configuration, a D-shaped configuration, a C-shaped configuration, a circular configuration, or the like.

The term "particle" refers to a discrete unit of matter. For example, particles may include atoms, ions, molecules, cells, agglomerates, or the like. Particles may also refer to (macro) molecular species such as proteins, enzymes, polynucleotides, or the like. Particles are typically between <NUM> and <NUM> in diameter. In some embodiments, particles are between <NUM> and <NUM> in diameter. In further embodiments, particles are between <NUM> and <NUM> in diameter. Particles may be naturally occurring or synthetic, or may combine natural and synthetic components within a single particle. Particles may refer to biological particles. For example, particles may include cells (for example, blood platelets, white blood cells, tumorous cells or embryonic cells, spermatozoa, to name a few), liposomes, proteoliposomes, yeast, bacteria, viruses, pollens algae, or the like. Particles may also refer to non-biological particles. For example, particles may include metals, minerals, polymeric substances, glasses, ceramics, composites, or the like.

The term "sensor," as used herein, refers to a device for measuring one or more characteristics of an object, such as a particle.

The terms "upstream" and "downstream" are referenced relative to a directional flow of particles in a flow-path and not particular elements or features within an apparatus.

With initial reference to <FIG>, an exemplary particle processing system <NUM> is depicted. The particle processing system <NUM> includes a particle sorting system <NUM> and a sort monitoring system <NUM>.

The particle sorting system <NUM> generally provides for particle sorting, for example, according to the detect-decide-deflect principle ((i) detection of one or more predetermined characteristics of a particle such as optical absorption, light scatter, extinction, polarization, fluorescent intensity, size, shape, charge, magnetic field, or the like; (ii) evaluation of the particle based on the detected characteristic(s); and (iii) sorting of the particle based on the evaluation thereof). The particle sorting system <NUM> may typically include a primary sensor system <NUM> (also referred to as a sort sensor system) for detecting one or more predetermined characteristics for a particle. The particle sorting system <NUM> may also typically contain a sort module <NUM>, including a sorter, for sorting a particle, for example, based at least in part on the one or more predetermined particle characteristics detected by the primary sensor system <NUM>. In some embodiments, the primary sensor system <NUM> may be operatively coupled directly or indirectly to the sort module <NUM>. In other embodiments, the primary sensor system <NUM> may be included in the sort module <NUM>.

In exemplary embodiments, the particle sorting system <NUM> may include a plurality of primary sensor systems and/or a plurality of sort modules. For example, the particle sorting system <NUM> may include a plurality of primary sensor systems, each included in or operatively coupled directly or indirectly to a sort module. In some embodiments, the plurality of primary sensor systems may be included in or operatively coupled directly or indirectly to a same sort module, for example, for detection of different particle characteristics. In other embodiments, the plurality of primary sensor systems may be included in or operatively coupled directly or indirectly to different sort modules. It is also noted that in some embodiments a primary sensor system may be included in or operatively coupled directly or indirectly to a plurality of sort modules, for example, to make optimal use of space in the particle sorting system <NUM>.

In exemplary embodiments, the particle sorting system <NUM> may include or be operatively associated directly or indirectly with a sort controller <NUM>, for example, for controlling the primary sensor system <NUM> and the sorter of the sort module <NUM>. One of ordinary skill in the art will be able to appreciate that sort controller <NUM> may be implemented in whole or in part via programming associated with a programmable processor, for example, processor <NUM>.

The sort monitoring system <NUM> is configured to monitor and, in some embodiments, evaluate and possibly take an action based on the evaluation of an operational characteristic of the particle sorting system <NUM>. The sort monitoring system <NUM> may include a secondary sensor system <NUM> (also referred to as a monitor sensor system) for monitoring particles downstream of the sorter of the sort module <NUM>. The sort monitoring system <NUM> may monitor an operational characteristic of the particle sorting system <NUM>, based on the secondary sensor system <NUM>, for example, based on the secondary sensor system <NUM> detecting a presence of a particle or an absence of a particle downstream of the sorter.

In exemplary embodiments, the sort monitoring system <NUM> may include or be operatively associated directly or indirectly with a monitoring system controller <NUM>. The monitoring system controller <NUM> may be responsive to the secondary sensor system <NUM>. In exemplary embodiments, the monitoring system controller <NUM> may be configured to evaluate an operational characteristic or a set of operational characteristics of the particle sorting system <NUM> and take an action, based on a result of the evaluation. For example, the monitoring system controller <NUM> may be configured to notify a user about and/or adjust, optimize, maintain or track the performance of an operation of the particle sorting system <NUM> based on the evaluation of the operational characteristic or the set of operational characteristics, for example, if a sort error is detected. As with the sorting system controller <NUM>, the monitoring system controller <NUM> may be implemented in whole or in part via programming associated with a programmable processor, for example, the same processor <NUM> as sort controller <NUM> or a different programmable processor. The monitoring system controller <NUM> may further include or be associated with a user interface <NUM>.

With reference to <FIG> an exemplary embodiment of a sort module <NUM> for a particle sorting system, for example, the particle sorting system <NUM> of <FIG>, is depicted. The sort module <NUM> includes a branched channel flow-path <NUM>. In exemplary embodiments, the flow-path <NUM> may be a flow-channel, for example, a microchannel, defined in a substrate, for example, of a microfluidic chip. The sort module <NUM> may be configured to receive a stream of particles suspended in a carrier fluid, through the flow-path <NUM> in a flow direction. The flow-path <NUM> may include a primary sensing region <NUM>, a sort region <NUM> in proximity to the primary sensing region <NUM> and a branch point <NUM> downstream of the sort region <NUM> where the flow-path <NUM> branches into a plurality of output branch channels <NUM>. A secondary detection region <NUM> may be included extending downstream of the sort region <NUM>, for example downstream of the branch point <NUM> along the output branch channels <NUM> of the flow-path <NUM>.

The sort module <NUM> may include or be operatively associated with a primary sensor system <NUM> for detecting particles at the primary sensing region <NUM>. The primary sensor system <NUM> may detect one or more predetermined particle characteristics which may serve as sorting criteria for the sort module <NUM>. In some embodiments, the primary sensor system <NUM> may detect particle velocity, for example, for controlling sort timing on a particle-by-particle basis. Sort module <NUM> may include or be operatively associated with a programmable processor <NUM> for controlling the primary sensor system <NUM>. In exemplary embodiments, the sort module <NUM> may include or be operatively associated with a plurality of primary sensor systems, for example, for detecting different particle characteristics.

The sort module <NUM> may include a sorter <NUM> for selectively sorting particles at the sort region <NUM>. For example, the sorter <NUM> may selectively sort a particle by deflecting it into one of the output branch channels <NUM> of the flow-path <NUM>. It is noted that while the sort module <NUM> depicted in <FIG> includes two output branch channels <NUM> for flow-path <NUM>, the present disclosure is not limited to such an embodiment. Indeed, in some embodiments (such as depicted in <FIG>) the flow-path <NUM> of the sort module <NUM> may include more than two output branch channels <NUM>. In exemplary embodiments, the sorter <NUM> may be configured such that deflected particles sort into one of one or more "active" branch channels of the flow-path <NUM> and non-deflected particles sort into one of one or more "passive" branch channels of the flow-path <NUM>.

In some embodiments, the sorter <NUM> may be responsive to an actuator which may be included in or operatively coupled directly or indirectly to the sort module <NUM>. In exemplary embodiments, the actuator may be a mechanical, optical, acoustic, magnetic, optomechanical, electromagnetic or other mechanism for deflecting or otherwise facilitating/enabling the sorting of a particle. The sort module <NUM> may include or be operatively associated with a programmable processor for controlling the sorter <NUM>, for example, the same processor <NUM> as for the primary sensor system <NUM> or a different programmable processor.

The sort module <NUM> may be associated with a sort monitoring system as described herein, for example, the sort monitoring system <NUM> of <FIG>. Thus, the sort module <NUM> may be associated with secondary sensor systems <NUM> for monitoring particles at the secondary detection region <NUM>. Thus, for example, each of the secondary sensor systems <NUM> may detect a presence of a particle or an absence of a particle, for one of the output branch channels <NUM>. In exemplary embodiments, a plurality of secondary sensor systems may be used for monitoring particles for a same one of the output branch channels <NUM>, for example, for detecting different particle characteristics. In some embodiments, the secondary sensors may be configured to monitor particles flowing from one of the output branch channels <NUM>, for example, in a capillary tube connected to the output branch channel.

With reference to <FIG> another exemplary embodiment of a sort module <NUM> for a particle sorting system is depicted. The sort module <NUM> includes a branched flow-path <NUM> including a primary sensing region <NUM> for association with a primary sensor system <NUM> a sort region <NUM> in proximity to the primary sensing region <NUM> and a branch point <NUM> downstream of the sort region <NUM> where the flow-path <NUM> branches into a plurality of output branch channels <NUM>. As depicted in <FIG>, the flow-path <NUM> advantageously branched into three output branch channels <NUM>. The sort module <NUM> may include a sorter <NUM> for selectively sorting particles at the sort region <NUM>. For example, the sorter <NUM> may selectively sort a particle by deflecting it into one of the output branch channels <NUM> of the flow-path <NUM>. A secondary detection region <NUM> for association with secondary sensor systems <NUM> of a sort monitoring system may be included extending downstream of the sort region <NUM>. In particular, secondary sensor systems <NUM> may be used to monitor particles at the secondary detection region <NUM>.

With reference now to <FIG>, exemplary embodiments of a multiple-sorter particle sorting system <NUM> for a particle processing system, for example the particle processing system <NUM> of <FIG>, are depicted.

As depicted in <FIG>, the multiple-sorter particle sorting system <NUM> may include a first and second sort modules 1200a and 1200b arranged in parallel, for example, to the increase throughput of the particle sorting system <NUM>. The modules 1200a and 1200b may each include a parallel branched flow-path <NUM> including a primary sensing region <NUM>, a sort region <NUM> in proximity to the primary sensing region <NUM> and a branch point <NUM> downstream of the sort region <NUM> where the flow-path <NUM> branches into a plurality of output branch channels <NUM> (also referred to herein as "individual outputs"). Notably, each output branch channel of the first sort module 1200a may merge, for example, with a corresponding output branch channel of the second sort module 1200b to form a merged output <NUM> of the sort modules.

In exemplary embodiments, sort modules 1200a and 1200b may include or be operatively associated with one or more primary sensor systems <NUM> for detecting particles at the primary sensing region <NUM>. In some embodiments, such as depicted in <FIG>, each sort module may include or be operatively associated with a different primary sensor system. In other embodiments, such as depicted in <FIG>, a same primary sensor system may be configured to detect particles for both the first and second sort modules.

With reference again to <FIG>, each sort module 1200a and 1200b may include or be operatively associated with a sorter <NUM> for selectively sorting particles at the sort region <NUM>, for example, based on prior detection of one or more predetermined characteristics, for example, by the primary sensor system(s) <NUM>.

The particle sorting system <NUM> of <FIG> may advantageously be associated with a sort monitoring system as described herein, for example, the sort monitoring system <NUM> of <FIG>. Thus, secondary sensor systems <NUM> may be used to monitor particles, for example detect a particle or an absence of a particle, at secondary detection regions <NUM> downstream of the sorting of the particles by at least one of one of the sort modules 1200a and 1200b. In exemplary embodiments, the sort monitoring system may be configured to monitor particles from both individual outputs <NUM> of a sort module and merged outputs <NUM> of a plurality of sorter modules. For example, as depicted in <FIG>, a secondary sensor <NUM> may be included for each of the individual outputs <NUM> and each of the merged outputs <NUM>. Alternatively, the sort monitoring system may be configured to sense or detect particles from either individual or merged outputs. For example, as depicted in <FIG>, secondary sensor systems <NUM> may be included for a corresponding pair of the individual outputs <NUM> but are not included for the merged outputs <NUM>. Conversely, as depicted in <FIG>, secondary sensor systems <NUM> may be included for the merged outputs <NUM> but not for the individual outputs <NUM>. In some embodiments, such as depicted in <FIG>, a same secondary sensor may be configured to simultaneously monitor particles for a plurality of separate outputs for example, for a plurality of individual outputs <NUM> or from a plurality of merged outputs <NUM>.

Referring now to <FIG>, an exemplary embodiment of a multiple-sorter particle sorting system <NUM> is depicted including a first sort module 1200a and a second sort module 1200b, wherein the second sort module 1200b is downstream of the first sort module 1200a. The particle sorting system <NUM> of <FIG> may advantageously be associated with a sort monitoring system as described herein, for example, the sort monitoring system <NUM> of <FIG>. Thus, secondary sensor systems <NUM> may be used to monitor particles, for example detect a particle or an absence of a particle downstream of one and/or both of the sort modules 1200a and 1200b.

Exemplary sorters and particle sorting systems are described in <CIT> and <CIT>.

With reference now to <FIG>, an exemplary embodiment of sort monitoring system <NUM> for a particle processing system, for example the particle processing system <NUM> of <FIG>, is depicted. The sort monitoring system <NUM> is configurable to associate with a flow-channel/flow-path, for example, an individual output of a sort module or a merged output of a plurality of sort modules, for monitoring an operational characteristic of the sort module or the plurality of sort modules. As depicted, the sort monitoring system <NUM> includes a notch or other cutout <NUM> for receiving a rigid or flexible channel, being a capillary tube <NUM>, connected to the flow-channel/flow-path. Thus, the exemplary sort monitoring system <NUM> of <FIG> may advantageously be a modular component, for example, a self-contained, external and/or interchangeable component, of the particle processing system. Alternatively, an exemplary sort monitoring system of the present disclosure may be integral with a particle sorting system, for example, integrated on a microfluidic chip.

In exemplary embodiments, a sort monitoring system, for example, the sort monitoring system <NUM> of <FIG>, may be advantageously utilized to detect a baseline, for example an event rate, for an output branch, for example, an active output branch, of a sort channel. With reference to <FIG>, an exemplary sort monitoring system <NUM> may be utilized to monitor a baseline event rate for an output branch, for example, for an active output branch 1214a, of a particle processing system <NUM>. In exemplary embodiments, the sort monitoring system <NUM> may be used to detect a particle count, for example, using an optical sensor system <NUM> such as depicted in <FIG>, for the active output branch 1214a during periods of no sorting through the active output branch 1214a. In exemplary embodiments, the baseline event rate is zero or close to zero demonstrating little to no leakage between output branches.

In exemplary embodiments, a sort monitoring system, for example, the sort monitoring system <NUM> of <FIG>, may be advantageously utilized to detect a baseline, for example, an event rate, for a merged output of a plurality of sort modules, for example a merged output <NUM> of <FIG>. Using a <NUM>-channel microfluidic chip (<NUM> sort parallel modules) tests were conducted to investigate the baseline event rate for a merged output before and after sequential periods of sorting through the output branches feeding the merged output. <FIG> depicts a counter strip chart for a particle count rate (and therefore event rate) for the merged output during two unique periods of sorting S1 and S2 through the output branches feeding he merged output. The baseline event rate B was observed to be less than <NUM> (<NUM> event per second) during periods of no sorting through the output branches feeding the merged output. Once the first period of sorting S1 was initiated, the event rate was observed to increase logarithmically (due to spread of velocities and therefore observed particle concentration per unit volume of fluid downstream of sorting). During S1, sorting was maintained at approximately <NUM> particles per second. After S1, sorting was halted the event rate was observed to decline exponentially again reaching the baseline event rate B as the slug of sorted particle-containing fluid exits the merged output. The second period of sorting S2 was observed to mirror the performance of S1 thus evidencing reproducibility.

In exemplary embodiments a visual display device, for example, visual display device <NUM> of <FIG>, may be used to monitor in real time or near real time an event rate, for example, a baseline event rate such as event rate B of <FIG>, for a sort channel and/or an output branch thereof. <FIG>, depict using an exemplary display device <NUM> to monitor events representing instances of particles detected in an active output branch. As depicted in <FIG>, an absence of events is depicted a during period of no active sorting through the active output branch. Any observed event E in the active output branch during a period of no active sorting through the active output branch (see <FIG>) is likely an outlier representing, for example, a residual or carry-over particle such as from a prior period of sorting through the active output branch. It is noted that in the event that As depicted in <FIG> a much higher frequency of events E is observed during a period of sorting through the active output branch. <FIG> depicts a zoomed in view of one of the observed events E in <FIG>. Based on the frequency of events during periods of sorting and no sorting through an active output branch, a baseline margin of error for sort rate may be determined. Notably, the greater the sort rate during active sorting through the active output branch the lower the baseline margin of error. In experiments conducted using a <NUM> channel microfluidic sorter chip, particles were sorted over a range of actuation rates from less than <NUM> to over <NUM> to test the dynamic range of the system.

Sensor systems, according to the present disclosure, for example primary and secondary sensor systems, may be any sensor system for monitoring a particle including but not limited to optical sensor systems, electrical sensor system, magnetic sensor system, acoustic sensor systems and the like. Sensor systems may advantageously be used to detect a particle or an absence of a particle in a flow-channel/flow-path, for example, based on light intensity observed through one or more pin holes. Sensor systems may further be used to detect one or more particle characteristics, for example, for facilitating identification/classification of particles.

Exemplary optical sensor system configurations are provided in <FIG>. In general, these configurations involve electromagnetic radiation illumination of particles in a flow-channel/flow-path <NUM>, for example, using a coherent or incoherent light source with or without additional light focusing elements, and the detection of light interaction (scatter, absorption, extinction, reflection, refraction, diffraction, fluorescence, plasmonic) in one or more directions. As depicted, each configuration includes a light source <NUM> (which may be a coherent light source, for example, a laser or an incoherent light source, for example, a light emitting diode), focusing optics <NUM> (for example single or multiple refractive, reflective, diffractive and or fiber optic elements) and an optical sensor <NUM> (for example, a photodiode, photomultiplier tube, pyroelectric detector, bolometer, APD, multiple-pixel photon counter, or CCD array). In exemplary embodiments, the monitoring system configuration may include or be operatively associated with a data acquisition system <NUM>, to count the number and/or monitor the magnitude of the electrical pulses produced by the sensor <NUM>. Pulse detection may be performed with analog circuitry, for example, a threshold detector, or digital circuitry, for example, an A/D converter, or digital counting circuitry. A programmable processor may then be used to display, analyze, and document the enumeration results, for example in order to detect a presence or an absence of particle or identify/classify a particle.

With specific reference to <FIG>, traverse flow axis and flow axis views of an exemplary configuration for an optical sensor system <NUM> are depicted, wherein particle detection is measured by the amount of light attenuated by a particle that traverses an optical beam that intersects the fluidic stream. The optical sensor <NUM> of the optical sensor system configuration depicted in <FIG> may be sensitive to extinction signal levels generated by a particle and may typically be positioned to collect light substantially in line with the illumination axis of the light source <NUM>. It is noted that in other exemplary embodiments the optical sensor <NUM> may be positioned to collect light at different angles relative to the illumination axis (see, for example <FIG>). The exemplary sort monitoring system <NUM> depicted in <FIG> may include a monitor sensor system <NUM> with a similar configuration to optical sensor system configuration depicted in <FIG> (for example, the monitor sensor system <NUM> includes a light source <NUM>, focusing optics <NUM>, an optical detector <NUM> and a data acquisition system <NUM>).

With specific reference to <FIG>, traverse flow axis and flow axis views of an exemplary configuration for an optical sensor system <NUM> are depicted, wherein particle detection measured by the amount of light scattered in the forward direction by a particle that traverses an optical beam that intersects the fluidic stream. The optical sensor <NUM> of the optical sensor system configuration depicted in <FIG> may be sensitive to forward scatter signal levels generated by a particle and may typically be positioned to collect light substantially in line with the illumination axis of the light source <NUM>. It is noted that in other exemplary embodiments, the optical sensor <NUM> may be positioned to collect light at different angles relative to the illumination axis (see, for example <FIG>). The optical sensor system <NUM> of <FIG> may include an obscuration element <NUM>, such as an obscuration disk, obscuration bar, obscuration mask, or the like, for blocking direct illumination light exiting the flow-channel/flow-path <NUM> from entering the sensor <NUM>. The detection angle of the sensor <NUM> may be larger than the obscuration angle of the illumination beam. In exemplary embodiments, a lens system may be included in between the flow-channel/flow-path <NUM> and the detector to maximize scattered light collection from the particle.

With specific reference to <FIG>, traverse flow axis and flow axis views of an exemplary configuration for an optical sensor system <NUM> are depicted, wherein particle detection is measured by the amount of fluorescent light emitted by a particle excited by an optical beam. As depicted, the fluorescence detection angle is in the forward direction relative to the excitation beam. The optical sensor <NUM> of the optical sensor system configuration depicted in <FIG> may typically be positioned to collect light substantially in line with the illumination axis of the light source <NUM>. It is noted that in other exemplary embodiments, the optical sensor <NUM> may be positioned to collect light at different angles relative to the illumination axis (see, for example <FIG>). In exemplary embodiments, a detected particle may be labeled with a fluorophore to enhance fluorescence detection. Otherwise, detection of intrinsic autofluorescence from the particle may be used. The optical sensor system <NUM> of <FIG> may include a spectral selection component <NUM>, for example, a refractive, diffractive, or interference filter or other spectrally selective element, between the flow-channel/flow-path <NUM> and the sensor <NUM> to select an optimal spectral range for fluorescent light emitted by a particle, and to attenuate excitation light from entering the sensor <NUM>. In exemplary embodiments, a second spectral selective component may be placed in between the light source <NUM> and the flow-channel/flow-path <NUM> configured to select an optimal spectral range for fluorescence excitation.

With specific reference to <FIG>, traverse flow axis and flow axis views of an exemplary configuration for an optical sensor system <NUM>, wherein particle detection is measured by side scatter or side florescence. The optical sensor <NUM> of the optical sensor system configuration depicted in <FIG> may typically be positioned to collect light at an angle to the illumination axis, for example, substantially orthogonal to the illumination axis.

In the case of scatter detection, the optical sensor system <NUM> of <FIG> may include, an obscuration element <NUM> for blocking direct illumination light exiting the flow-channel/flow-path <NUM> from entering the sensor <NUM>. The detection angle of the sensor <NUM> may be larger than the obscuration angle of the illumination beam. In exemplary embodiments involving scatter detection, a lens system may be included in between the flow-channel/flow-path <NUM> and the detector to maximize scattered light collection from the particle.

In the case florescence detection, the optical sensor system <NUM> of <FIG> may include a spectral selection component <NUM>, for example, a refractive, diffractive, or interference filter or other spectrally selective element, between the flow-channel/flow-path <NUM> and the sensor <NUM> to select an optimal spectral range for fluorescent light emitted by a particle, and to attenuate excitation light from entering the sensor <NUM>. A second spectral selective component may be placed in between the light source <NUM> and the flow-channel/flow-path <NUM> configured to select an optimal spectral range for fluorescence excitation.

As noted above, sensor system configurations are not limited to optical configurations. Indeed, other sensing approaches may be applied instead of or in conjunction with optical means. These sensing approaches may include but are not limited to (i) passive or active electrical detection including but not limited to conductance, capacitance, RF field monitoring through devices fabricated on the microchip, or located off-chip near channels of interest (ii) magnetic detection, such as using a Hall-effect device or other field probes located in the proximity of flow-channels and (iii) acoustic detection such as ultrasound absorption, reflection, scatter or the like using on-board or remote devices. Other optomechanical or electromagnetic sensing systems may also be employed.

In exemplary embodiments, a particle may be detected by an analog level, for example by surpassing (going above or below) a threshold which produces a detectable voltage change. The signal may be used to characterize, identify or count the particle. Temporal information may be used to determine the velocity of the particle, the time elapsed from the detection of the particle at another location, the expected time that the particle will reach a selected position, or the like.

In exemplary embodiments, conductive traces may be used to form an electrode array across or along one or more flow paths where the absence or presence of a particle adjusts the conductivity or other electrical measurement, for example, capacitance, resistance, inductance of the fluid path between any electrode pair. The conductive traces may be formed on one substrate of a microfluidic chip prior to fusing a second substrate to provide contact with flow path. As a particle flows near or between electrodes, the conductivity of electricity of the electrical circuit may change and be detected with appropriate electronic processing tools such as an analog current meter or a computer.

Referring now to <FIG>, an exemplary electrode array <NUM> is depicted. The electrode array <NUM> includes a pair of conductive traces <NUM> which may be operatively associated with, for example, electrically coupled to, an electric property detector such as a current meter, capacitance meter, ohm meter, inductance meter, multi-meter, or the like. Electric properties which may be detected include but are not limited to conductivity, capacitance, resistance, inductance, and the like. As depicted the electrode array <NUM> includes a pair of conductive traces <NUM> associated with a channel or channels. For example, the conductive traces <NUM> may be positioned adjacent or across a channel. In exemplary embodiments the conductive traces <NUM> may be associated with, one, two or all output branch channel(s) (for example, output branch channel <NUM>) of a sort module <NUM>. Thus, a particle or an absence of a particle in a channel, for example in the output branch channel <NUM>, may be detected by monitoring changes in electric properties of the array <NUM> such as changes in changes in electric properties between the traces <NUM>. In some embodiments, the array <NUM> includes a plurality of conductive trace pairs. In some embodiments, a first of the plurality of conductive trace pairs are configured to detect or measure a first selected electric property of a particle and a second of the plurality of conductive trace pairs is configured to detect or measure a second selected electric property of a particle.

In exemplary embodiments, the electrode array <NUM> may be associated with active and/or a passive output branch channels of the sort module <NUM>. The electrode array <NUM> may further be associated with output branch channels of a plurality of sort modules. In some embodiments, the electrode array <NUM> may include a plurality of pairs of conductive traces each associated with a different channel, for example a different output branch channel of a sort module <NUM> or a plurality of sort modules. In other embodiments, in order to conserve space, the electrode array may include a pair of conductive traces associated with a plurality of channels, for example, a plurality of output branch channels of a sort module <NUM> or sort modules. In some embodiments, the electrode array may include a pair of conductive traces associated with a plurality of passive output branch channels of a sort module <NUM> or sort modules. In other embodiments, the electrode array may include a pair of conductive traces associated with a plurality of active output branch channels of a sort module <NUM> or sort modules. In exemplary embodiments where the electrode array includes a pair of conductive traces associated with a plurality of channels, channels may be distinguished, for example, based on particle spacing/timing and/or modulation signatures for different channels.

Referring now to <FIG>, an electric array <NUM> may be adapted detect particles in multiple output branch channels of a particle sorting system <NUM>. Thus, the electric array <NUM> may include a plurality of pairs of conductive traces, e.g., wherein one or both traces for each pair are independently coupled to a detector for detecting an electric property, for example, across a corresponding one of the output branch channels. Advantageously one trace in each pair may share a common ground <NUM>.

The monitor system configurations and sensing approaches described may be applied for both modular and integrated embodiments of the sort monitoring system. It will be appreciated by one of ordinary skill in the art that particle detection may be measured by any combination of sensor configurations. The use of multiple parameter detection may advantageously enable detection of subpopulations of sorted particles.

In exemplary embodiments, the sort monitoring system of the present disclosure may be used to monitor and, in some instances, evaluate and/or control, a baseline presence/absence of microparticles for an output of a sort module or a group of sort modules for a given sorter state. Baseline monitoring may be established before, during, between, or after sorting functions. Sorting conditions may then be adjusted to regulate the baseline, for example, for a selected sort mode such as purification, recovery, enrichment, or the like. Baseline monitoring may also be used to determine whether a sort may begin, continue or halt, or whether additional actions may be required (for example a cleaning step).

The sort monitoring system of the present disclosure may also be used to count (instead of or in addition to detecting a particle characteristic, for example size, velocity, position, time or flight, granularity, light scatter, fluorescence, magnetism, conductivity, capacitance, acoustic properties, or the like) the number of particles (sorted or otherwise) for one, many, or all outputs, including individual and merged outputs, of a sort module or a group of sort modules. A monitored particle count may then be compared to an expected particle count, for example, post-sort particle counts may be compared to pre-sort particle counts. The sort monitoring system may be used to determine, for example, actual versus expected sort rate, quantity, quota (i.e. if sufficient particles of a particular type have been isolated, and measured to have been isolated) and other sort statistics/information. The sort monitoring system may further be configured to identify and, in some instances, track one, many, or all particles that flow through the particle sorting system, for example, to aid in monitoring production/processing of multiple sort samples from a single input sample, to verify marking of a particle (changing the state of a particle as part of the sort operation), or to estimate and or determine a composition of a sample that has been processed by a particle sorting system.

Various operational characteristics of a particle processing system may be controlled based on the sort monitoring system. The sort monitoring system may be configured to begin, continue or halt, a sorting operation or take an additional action (for example a cleaning step) based on a monitored operational characteristic. In multiple-channel and multiple-sorter systems, sorter operations in a selected flow-channel/sort module may be adjusted (while other flow-channels/sort modules remained unaffected).

In exemplary embodiments, the sort monitoring system may be configured to adjust a sample introduction rate and/or fluid flow into a sort module or a group of sort modules, for example, to ensure optimal sort performance throughout a sort. For example if a particle sorting system is overperforming, in terms of accuracy, the sort monitoring system may be configured to increase throughput, for example, by increasing sample introduction rate and/or fluid flow. Conversely, if the particle processing system is underperforming in terms of accuracy, for example, due to sort timing errors resulting from false or missed sort, the sort monitoring system may be configured to decrease sample introduction rate and/or fluid flow thereby reducing the incidence of coincidence events. The particle processing system may also be configured to calibrate/optimize the delay timing calculation based on the detection of coincidence events.

In exemplary embodiments, as noted above, a sorting system may be configured to simultaneously sort multiple samples (sources) and/or utilize multiple sorters, for example, within a single microfluidic substrate. The sort monitoring system of the present disclosure may be configured to monitor individual performance of one, many or all sorters/flow-channels and/or combined performance of a plurality of sorters/flow-channels. This versatility is particularly useful for monitoring sorters/flow-channels tasked with different operating criteria/conditions and for managing multiple-sorter/channel systems. For example, a combined performance of a multiple-channel system may serve as a preliminary threshold, for example, to determine whether to take an action such as halting one of the sorters/flow-channels. Individual performances of each flow-channel may then be used to assess which of the channel or channels to act upon. In this way, flow-channels with superior performance compensate for flow-channels with inferior performance (provided that the combined output is within acceptable tolerances) and a high throughput is maintained.

In exemplary embodiments, the sort monitoring system may be used to monitor/optimize a multiple-particle distribution (population statistics). The sort monitoring system may be configured to monitor/adjust a sort fraction in real-time. Optimal operational conditions for a desired sort fraction may also be stored for future use.

As noted above, the sort monitoring system of the present disclosure is able to provide knowledge of the yield, number, recovery and purity of particles after the sorting thereof. The sort monitoring systems allows for efficient use of precious sample, instrument time, user time, consumable materials and other resources. For example, the sort monitoring system may be used to notify a user when a particular number of particles are isolated or to maximize a number of sorted samples from a single input for multiple-treatment or multiple-patient use. This is particularly, useful when dealing with precious, rare or high-value materials or when a subsequent step needs to be performed on a sorted fraction.

It is explicitly contemplated that the systems and methods presented herein may include one or more programmable processing units having associated therewith executable instructions held on one or more computer readable medium, RAM, ROM, harddrive, and/or hardware. In exemplary embodiments, the hardware, firmware and/or executable code may be provided, for example, as upgrade module(s) for use in conjunction with existing infrastructure (for example, existing devices/processing units). Hardware may, for example, include components and/or logic circuitry for executing the embodiments taught herein as a computing process.

Displays and/or other feedback means may also be included to convey detected/processed data. In exemplary embodiments, notifications may be displayed, for example, on a monitor. The display and/or other feedback means may be stand-alone or may be included as one or more components/modules of the processing unit(s). In exemplary embodiments, the display and/or other feedback means may be used to facilitate selection of one or more suggested actions based a detected operational characteristic of a particle sorting system.

The actual software code or control hardware which may be used to implement some of the present embodiments is not intended to limit the scope of such embodiments. For example, certain aspects of the embodiments described herein may be implemented in code using any suitable programming language type such as, for example, assembly code, C, C# or C++ using, for example, conventional or object-oriented programming techniques. Such code is stored or held on any type of suitable computer-readable medium or media such as, for example, a magnetic or optical storage medium.

As used herein, a "processor," "processing unit," "computer" or "computer system" may be, for example, a wireless or wire line variety of a microcomputer, minicomputer, server, mainframe, laptop, personal data assistant (PDA), wireless e-mail device (for example, "BlackBerry," "Android" or "Apple," trade-designated devices), cellular phone, pager, processor, fax machine, scanner, or any other programmable device configured to transmit and receive data over a network. Computer systems disclosed herein may include memory for storing certain software applications used in obtaining, processing and communicating data. It can be appreciated that such memory may be internal or external to the disclosed embodiments. The memory may also include storage medium for storing software, including a hard disk, an optical disk, floppy disk, ROM (read only memory), RAM (random access memory), PROM (programmable ROM), EEPROM (electrically erasable PROM), flash memory storage devices, or the like.

Referring now to <FIG>, an exemplary computing environment suitable for practicing exemplary embodiments is depicted. The environment may include a computing device <NUM> which includes one or more media for storing one or more computer-executable instructions or code for implementing exemplary embodiments. For example, memory <NUM> included in the computing device <NUM> may store computer-executable instructions or software, for example instructions for implementing and processing every module of the application <NUM>.

The computing device <NUM> also includes processor <NUM>, and, one or more processor(s) <NUM>' for executing software stored in the memory <NUM>, and other programs for controlling system hardware. Processor <NUM> and processor(s) <NUM>' each can be a single core processor or multiple core (<NUM> and <NUM>') processor. Virtualization can be employed in computing device <NUM> so that infrastructure and resources in the computing device can be shared dynamically. Virtualized processors may also be used with application <NUM> and other software in storage <NUM>. A virtual machine <NUM> can be provided to handle a process running on multiple processors so that the process appears to be using one computing resource rather than multiple. Multiple virtual machines can also be used with one processor. Other computing resources, such as field-programmable gate arrays (FPGA), application specific integrated circuit (ASIC), digital signal processor (DSP), Graphics Processing Unit (GPU), and general-purpose processor (GPP), may also be used for executing code and/or software. A hardware accelerator <NUM>, such as implemented in an ASIC, FPGA, or the like, can additionally be used to speed up the general processing rate of the computing device <NUM>.

The memory <NUM> may comprise a computer system memory or random access memory, such as DRAM, SRAM, EDO RAM, or the like. The memory <NUM> may comprise other types of memory as well, or combinations thereof. A user may interact with the computing device <NUM> through a visual display device <NUM>, such as a computer monitor, which may display one or more user interfaces <NUM>. The visual display device <NUM> may also display other aspects or elements of exemplary embodiments, for example, notifications. The computing device <NUM> may include other I/O devices such a keyboard or a multiple-point touch interface <NUM> and a pointing device <NUM>, for example a mouse, for receiving input from a user. The keyboard <NUM> and the pointing device <NUM> may be connected to the visual display device <NUM>. The computing device <NUM> may include other suitable conventional I/O peripherals. The computing device <NUM> may further comprise a storage device <NUM>, such as a hard-drive, CD-ROM, or other storage medium for storing an operating system <NUM> and other programs, for example, a program <NUM> including computer executable instructions for, monitoring, evaluating, or acting on an evaluation of an operational characteristic of a particle sorting system as taught herein.

The computing device <NUM> may include a network interface <NUM> to interface to a Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections including, but not limited to, standard telephone lines, LAN or WAN links (for example, <NUM>, T1, T3, 56kb, X. <NUM>), broadband connections (for example, ISDN, Frame Relay, ATM), wireless connections, controller area network (CAN), or some combination of any or all of the above. The network interface <NUM> may comprise a built-in network adapter, network interface card, PCMCIA network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device <NUM> to any type of network capable of communication and performing the operations described herein. Moreover, the computing device <NUM> may be any computer system such as a workstation, desktop computer, server, laptop, handheld computer or other form of computing or telecommunications device that is capable of communication and that has sufficient processor power and memory capacity to perform the operations described herein.

The computing device <NUM> can be running any operating system such as any of the versions of the Microsoft® Windows® operating systems, the different releases of the Unix and Linux operating systems, any version of the MacOS® for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. The operating system may be running in native mode or emulated mode.

<FIG> illustrates an exemplary network environment <NUM> suitable for a distributed implementation of exemplary embodiments. The network environment <NUM> may include one or more servers <NUM> and <NUM> coupled to clients <NUM> and <NUM> via a communication network <NUM>. In one implementation, the servers <NUM> and <NUM> and/or the clients <NUM> and/or <NUM> may be implemented via the computing device <NUM>. The network interface <NUM> of the computing device <NUM> enables the servers <NUM> and <NUM> to communicate with the clients <NUM> and <NUM> through the communication network <NUM>. The communication network <NUM> may include Internet, intranet, LAN (Local Area Network), WAN (Wide Area Network), MAN (Metropolitan Area Network), wireless network (for example, using IEEE <NUM> or Bluetooth), or other network configurations. In addition the network may use middleware, such as CORBA (Common Object Request Broker Architecture) or DCOM (Distributed Component Object Model) to allow a computing device on the network <NUM> to communicate directly with another computing device that is connected to the network <NUM>.

In the network environment <NUM>, the servers <NUM> and <NUM> may provide the clients <NUM> and <NUM> with software components or products under a particular condition, such as a license agreement. The software components or products may include one or more components of the application <NUM>. For example, the client <NUM> may evaluate an operational characteristic of a particle processing system over the server <NUM>.

With reference now to <FIG>, an exemplary method <NUM> for processing particles is depicted. The method <NUM> generally includes steps of sorting particles in a stream of particles suspended in a carrier fluid through a particle sorting system including a plurality of sorters (Step <NUM>) and monitoring an operational characteristic of the particle sorting system (Step <NUM>). In exemplary embodiments, the step of sorting particles (Step <NUM>) may advantageously include characterizing the particles and sorting the particles based on the characterization thereof. In further exemplary embodiments, the exemplary method <NUM> may further include steps of evaluating, for example, using a processor, the operational characteristic (Step <NUM>) and affecting an action, for example, a notification action or a corrective or proactive action based on such an evaluation (Step <NUM>).

Claim 1:
A particle processing system (<NUM>) comprising:
a plurality of microfluidic sort modules (<NUM>, <NUM>, 1200a, 1200b), each microfluidic sort module including a branched flow-channel (<NUM>) defined in a substrate and configured to receive a stream of particles, the branched flow-channel including a flow-path that branches at a branch point into a first output branch channel and a second output branch channel (<NUM>, <NUM>), and each microfluidic sort module includes a particle sorter operable to selectively sort particles between the first output branch channel and the second output branch channel (<NUM>, <NUM>) of the branched flow-channel;
a plurality of first sensor systems (<NUM>), each of the first sensor systems optically associated with a respective one of the plurality of microfluidic sort modules upstream of the branch point to sense a particle characteristic of a particle in the stream of particles flowing through the branched flow-channel, each of the first sensor systems having focusing optics (<NUM>) and a first optical sensor (<NUM>) to control a sorting operation for a respective one of the microfluidic sort modules;
a capillary tube (<NUM>) fluidically coupled to one of the first output branch channel or the second output branch channel of at least one of the plurality of microfluidic sort modules; and
a sort monitoring system (<NUM>) having a second sensor system (<NUM>) optically coupled to the capillary tube to monitor a performance of the sorting operation by detecting and collecting particle data that characterizes individual particles downstream of the particle sorter using focusing optics (<NUM>) to direct light through the capillary tube and onto a second optical sensor (<NUM>), to determine a statistically-based characteristic of a composition of a sorted sample from the data collected from the individual particles detected downstream of the particle sorter, and
wherein the sort monitoring system is configured to real-time evaluate the statistically-based characteristic of the composition of the sorted sample.