A method and system for single cell droplet sorting

Abstract

The application discloses a single-cell droplet sorting method and system, which comprises the following steps: generating droplets containing cells on a digital microfluidic chip, detecting the droplets to identify the number of cells in the droplets, and controlling the path of the droplets based on the identification result, guiding single-cell droplets into a collection area, guiding empty droplets into a waste liquid area, and guiding multi-cell droplets into a processing area. In the processing area, the multi-cell droplets are split into a plurality of sub-droplets after fusion with auxiliary droplets, so that the cells are redistributed in the sub-droplets; the detection and processing process is repeatedly performed on the sub-droplets until single-cell droplets are obtained or a preset condition is reached. Through the cyclic iteration of the droplet reconstruction and distribution mechanism, the application improves the acquisition efficiency of single-cell droplets and overcomes the problem that the recovery rate is low due to the limitation of the Poisson distribution in the traditional method.

Description

Technical Field

[0001] This invention relates to the fields of microfluidics and bioengineering, specifically to a method and system for sorting single-cell droplets. Background Technology

[0002] Microalgae, yeast, bacteria, and other single-celled organisms have significant applications in biobreeding, environmental monitoring, functional screening, and biomanufacturing. However, efficient and low-damage single-cell sorting remains a major technical challenge limiting these applications. Digital microfluidics, which enables droplet generation, transport, fusion, and splitting through electrowetting, offers advantages such as programmable control, low sample consumption, and easy integration with microscopic imaging systems, and is increasingly being applied to the field of single-cell droplet manipulation.

[0003] Existing droplet-based single-cell encapsulation methods typically employ random loading mechanisms, with the cell entry process governed by a Poisson distribution. When the encapsulation expectation is low, the proportion of empty droplets is high, leading to a low effective single-cell droplet yield. When the encapsulation expectation approaches 1, the proportion of multi-cell co-encapsulated droplets increases significantly, affecting single-cell sorting purity. For multi-cell droplets, current technologies often handle them by direct disposal, resulting in sample waste and further limiting single-cell recovery rates.

[0004] Furthermore, in digital microfluidic systems, the lack of an effective post-processing mechanism for multi-cell droplets can easily lead to a mismatch in processing capacity between droplet generation, detection, and post-processing, resulting in droplet congestion, waiting, and a decrease in overall throughput. Summary of the Invention

[0005] The purpose of this invention is to provide a single-cell droplet sorting method and system. Through a cyclic iterative droplet reconstruction and distribution mechanism, the efficiency of single-cell droplet acquisition is improved, overcoming the problem of low recovery rate caused by the limitation of Poisson distribution in traditional methods.

[0006] The first aspect of the present invention discloses a single-cell droplet sorting method, which applies a controlled external force to the droplets to cause multi-cell droplets to be redistributed after fusion and division, and returned to the detection area for cyclic sorting until a preset termination condition is reached, so as to realize the recycling of droplets and the extraction of single cells, including: S1: generating sample droplets containing cells to be sorted in the droplet generation area.

[0007] S2: The sample droplet is transported to the detection area, the droplet is image acquired, and the number or type of cells in the droplet is identified.

[0008] S3: Perform path control on the droplets based on the recognition results, wherein: when the number of cells in the droplet is 1, the droplet is transported to the collection area; when the number of cells in the droplet is 0, the droplet is transported to the waste liquid area; when the number of cells in the droplet is greater than 1, the droplet is transported to the reconstruction processing area.

[0009] S4: In the reconstruction processing area, the droplets are driven by a driving control method to cause the droplets to fuse and split, forming multiple sub-droplets, so that the multiple cells in the droplets are redistributed among the sub-droplets.

[0010] S5: Retransmit the sub-droplets to the detection area and determine whether the preset termination condition has been met; if the preset termination condition has not been met, return to step S2 to continue execution; if the preset termination condition has been met, end the sorting process.

[0011] According to one embodiment of the present invention, the generation, transport, fusion and splitting of the sample droplets are all achieved by applying controlled external forces to the droplets.

[0012] According to one embodiment of the present invention, the controlled external force includes electric field force, acoustic field force, magnetic field force, pressure, or a combination thereof.

[0013] According to one embodiment of the present invention, the controlled external force is achieved by an electrowetting driving force generated by an electrode array on a digital microfluidic chip controlled by a drive module.

[0014] According to an embodiment of the present invention, in step S1, the droplet generation region controls the meniscus electrode structure 1 to generate the sample droplet through the driving module; the meniscus electrode structure 1 includes a liquid storage electrode, a contraction channel and a meniscus cutting region.

[0015] According to one embodiment of the present invention, the fusion process in step S4 includes fusing the droplet with at least one auxiliary droplet, the auxiliary droplet including a culture medium droplet, a buffer droplet, or a diluent droplet.

[0016] According to one embodiment of the present invention, the reconstruction processing region includes a dual-channel mixing and splitting region, which is used to fuse multicellular droplets and auxiliary droplets to form a reconstructed droplet under the action of the driving module, and to split the fused reconstructed droplet into two or more sub-droplets.

[0017] According to one embodiment of the present invention, the preset termination condition includes any one or a combination of the following: obtaining a preset number of single-cell droplets, reaching a preset number of iterations, the number of droplets to be processed being lower than a set threshold, or the single-cell recovery rate reaching a set value.

[0018] According to one embodiment of the present invention, before generating sample droplets containing cells to be sorted, the method further includes: determining a cell encapsulation expectation value based on the droplet generation rate and the splitting processing capacity of the remodeling processing zone, and adjusting the concentration of the cell suspension to be sorted based on the cell encapsulation expectation value so that the droplet processing load entering the remodeling processing zone matches its processing capacity.

[0019] According to one embodiment of the present invention, step S2 analyzes the droplet image using an image processing method to identify the number or type of cells within the droplet; the image processing method includes identification based on edge features, area features, morphological features, or roundness features.

[0020] According to one embodiment of the present invention, the cells to be sorted are microalgae, single-celled microorganisms or other single-celled organisms.

[0021] A second aspect of the present invention discloses a single-cell droplet sorting system for the above-described method, comprising: a digital microfluidic chip, a driving module, an image acquisition module, an image processing module, a controller, and a collection module, wherein the digital microfluidic chip is provided with a droplet generation area, a detection area, a reconstruction processing area, a collection area, a waste liquid area, and a reflux path for returning sub-droplets to the detection area.

[0022] The driving module is used to drive the generation, transport, fusion, splitting and path switching of droplets on the digital microfluidic chip.

[0023] An image acquisition and recognition module is used to acquire images of droplets within the detection area and identify the number and / or type of cells within the droplets to obtain droplet recognition results. A controller is used to control the drive module to perform path scheduling based on the droplet recognition results. The path scheduling includes: guiding target single-cell droplets to a collection area; guiding empty droplets or non-target droplets to a waste liquid area; guiding multi-cell droplets to a reconstruction processing area; controlling the multi-cell droplets to fuse with at least one auxiliary droplet and split to form sub-droplets in the reconstruction processing area; and re-transporting the sub-droplets back to the detection area via the return path for re-identification and sorting. The collection module is used to collect target single-cell droplets.

[0024] According to one embodiment of the present invention, the droplet generation region is provided with a meniscus electrode structure 1, the meniscus electrode structure 1 including a liquid storage electrode, a contraction channel and a meniscus cutting region.

[0025] According to one embodiment of the present invention, the path scheduling module is configured to: schedule the droplet to the collection area when the number of cells in the droplet is 1; schedule the droplet to the waste liquid area when the number of cells in the droplet is 0; and schedule the droplet to the reconstruction processing area when the number of cells in the droplet is greater than 1.

[0026] According to one embodiment of the present invention, the lower substrate includes an electrode layer, a dielectric layer and a hydrophobic layer.

[0027] According to one embodiment of the present invention, the reconstruction processing region includes a dual-channel hybrid splitting region, the dual-channel hybrid splitting region including a confluence region and hybrid splitting electrodes located on both sides of the confluence region.

[0028] According to one embodiment of the present invention, the image acquisition module includes a microscopic imaging device and an image sensing device.

[0029] According to one embodiment of the present invention, the driving module outputs a driving electrical signal to realize droplet generation, transport, fusion and splitting.

[0030] According to one embodiment of the present invention, the digital microfluidic chip is provided with one or more reconfiguration processing areas.

[0031] The technical effects achieved by this invention are as follows: First, the detected multi-cell droplets are no longer directly discarded, but are transformed into multiple sub-droplets that can participate in sorting through droplet fusion, division and cyclic redistribution. This increases the probability of obtaining the target single-cell droplet and reduces sample waste, which is especially suitable for screening scenarios of precious samples, low-abundance samples and high-value strains.

[0032] Secondly, existing droplet single-cell encapsulation methods are usually limited by random encapsulation mechanisms, making it difficult to balance single-cell yield and sorting purity. This invention improves single-cell droplet output efficiency without relying on extremely low encapsulation concentrations by reconstructing multi-cell droplets and combining iterative sorting, thereby effectively reducing the proportion of empty droplets and multi-cell droplets.

[0033] Furthermore, droplet path scheduling is performed based on the detection results, and dynamic matching between droplet generation, detection, reconstruction and collection processes is achieved through a cyclic processing mechanism, thereby reducing droplet congestion, waiting and stagnation within the chip and improving system throughput and operational stability.

[0034] Finally, droplet-level manipulation based on digital microfluidic chips features low sample consumption, good sealing, and easy integration. It can be combined with microscopic imaging, image recognition, and automatic control systems to complete sorting while maintaining cell viability. It is suitable for screening, culturing, and analyzing various single-cell objects and has good application value. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1This is a flowchart of the single-cell droplet sorting method disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the crescent-shaped generation structure disclosed in an embodiment of the present invention; Figure 3 This is a block diagram of the single-cell droplet sorting system disclosed in an embodiment of the present invention; Figure 4 This is a functional layout diagram of the digital microcontroller chip for the single-cell droplet sorting system disclosed in an embodiment of the present invention; Figure 5(a) is a schematic diagram of the mixed droplet splitting process and queuing theory parameter optimization of the single-cell droplet sorting system disclosed in the embodiment of the present invention; Figure 5(b) shows the system output single-cell droplet velocity under different parameter conditions disclosed in the embodiments of the present invention; Figure 5(c) shows the relationship between the average number of cell divisions and the expected cell encapsulation value λ in the single-cell droplet sorting system disclosed in this embodiment of the invention. Figure 6 This is a performance evaluation chart showing the comparison of purity and recovery rate of different algal samples under single-sorting and cyclic iterative sorting conditions in the single-cell droplet sorting system disclosed in this embodiment of the invention.

[0036] Figure label: 1-Crescent-shaped electrode structure, 2-Electrode spacing, 3-Electrode linewidth. Detailed Implementation

[0037] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0038] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0039] In this invention, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist.

[0040] like Figure 1As shown, the present invention discloses a digital microfluidic single-cell droplet sorting method. Compared with the traditional approach of reducing cell concentration, actively encapsulating, or discarding multi-cell droplets after identification, this application treats multi-cell droplets as reprocessable objects and transforms the originally discarded multi-cell droplets into single-cell droplets through dilution fusion, binary random redistribution, and repeated division detection.

[0041] By applying a controlled external force to the droplets, the multi-cell droplets are fused and divided, then redistributed and returned to the detection area for cyclic sorting until a preset termination condition is reached, thereby realizing the recycling of droplets and the extraction of single cells; the controlled external force includes electric field force, acoustic field force, magnetic field force, pressure, or a combination thereof.

[0042] In this embodiment, an electric field is preferably used to drive the generation, transport, fusion and splitting of droplets. In other embodiments, an electric field, acoustic field, magnetic field, pressure or a combination thereof can be used to apply controlled external force to the droplets to achieve the same or similar droplet manipulation process.

[0043] The specific steps include: S1: Generating sample droplets containing cells to be sorted using a droplet generation structure. Sample droplets containing cells to be sorted are generated in the droplet generation area of ​​the digital microfluidic chip. The sample suspension containing the target cells is loaded into the sample inlet of the digital microfluidic chip, and droplets are formed with the carrier liquid via electrode driving, resulting in multiple droplets to be detected. The droplets to be detected are then controlled to move sequentially to the detection area.

[0044] In this embodiment, sample droplets to be sorted are first prepared. These droplets contain target cells, such as microalgae cells or other single-celled organisms. The droplets are generated through a droplet generation region in a digital microfluidic chip, using the electrowetting drive principle to generate individual droplets. The chip consists of an upper substrate, a lower substrate, and a gap layer. The lower substrate includes an electrode layer, a dielectric layer, and a hydrophobic layer.

[0045] The droplet generation process is controlled by a voltage of 80V and a frequency of 1kHz. The generated droplets are transmitted to the detection area via the control system. The droplet generation rate is 30 droplets per minute.

[0046] The droplet generation region employs a meniscus-shaped droplet generation electrode structure. This structure consists of a liquid storage electrode, a contraction channel, and a meniscus-shaped cutting area. Through geometric constraints, it enables the droplets to neck and separate at a fixed position, thereby improving the stability and consistency of microdroplet generation.

[0047] The feature dimension of its separating electrode tip is designed to be 0.3 mm, with a lead width of 40 μm, enabling the generation of microdroplets of approximately 10 nL. Dielectric layer fabrication: SU-8 2005 photoresist was selected as the dielectric layer material. A dense dielectric layer with a thickness of approximately 5 μm was formed through spin coating, soft baking, exposure, development, and hard baking (150℃, 30 min). Compared to silicon nitride and PDMS, SU-8 offers lower fabrication costs and good light transmittance while maintaining insulation, and achieves effective contact angle modulation at a driving voltage of 80V. Channel height control: To ensure the stability of microdroplet generation, 60 μm diameter polystyrene microspheres mixed with UV adhesive were used as a support, and the channel height h between the upper and lower electrodes was strictly limited.

[0048] Alternatively, in the droplet generation region, a meniscus electrode structure 1 can be used to generate droplets. Geometric constraints can cause the liquid to neck and separate at a predetermined position, thereby improving the stability of droplet generation.

[0049] S2: The sample droplet is transported to the detection area, the droplet is image acquired, and the number or type of cells in the droplet is identified.

[0050] In the detection area, droplet images are acquired using a microscopic imaging device, and the control system identifies the droplet images to obtain information on the number of cells and / or cell type within the droplet.

[0051] After the droplets are transported to the detection area, images of each droplet are acquired using a microscope imaging device. The image sensing device employs a high-resolution CMOS sensor for image capture. After image acquisition, the image processing module analyzes the droplets to identify the number and type of cells within them. Specifically, edge detection and Hough transform algorithms are used to ensure accurate identification of droplet morphology, thereby enabling cell counting.

[0052] In step S2, the number of targets and edges in the droplet image are determined by image processing or machine vision methods. Image recognition methods can include threshold segmentation, edge extraction, contour analysis, convolutional neural network recognition, etc.

[0053] For example, the number of cells in a droplet can be identified by counting cell outlines, and algal cells can be distinguished from bacterial cells by morphological characteristics. Area, shape, or roundness features can be used for identification to obtain the number and type of cells within the droplet.

[0054] S3: Based on the identification results in step S2, the droplet is path-controlled and enters different processing zones. Specifically: when the number of cells in the droplet is 1, the droplet is transported to the collection zone; when the number of cells in the droplet is 0, the droplet is transported to the waste liquid zone; when the number of cells in the droplet is greater than 1, the droplet is transported to the reconstruction processing zone.

[0055] When the identification result indicates that the droplet contains only one target cell, the droplet is driven to the collection area and stored as a single-cell target droplet or entered into the subsequent culture process.

[0056] S4: In the reconstruction processing area, the droplets are driven by a driving control method to cause the droplets to fuse and split, forming multiple sub-droplets, so that the multiple cells in the droplets are redistributed among the sub-droplets.

[0057] In the reconstruction processing zone, the droplet to be processed and at least one auxiliary droplet are controlled by a drive module to contact and fuse under the action of electrodes to form a reconstructed droplet. Subsequently, a control signal is applied to the reconstructed droplet through an electrode array, causing the reconstructed droplet to deform and break at a predetermined position, forming multiple sub-droplets. During the splitting process, the cells in the original droplet are randomly distributed among the sub-droplets according to the liquid volume distribution.

[0058] When the identification results indicate that the droplet contains two or more cells, the droplet is transported to the droplet reconstruction region, where it is split to form at least two sub-droplets, or it is fused with an empty droplet and then split to form multiple sub-droplets.

[0059] The reconstruction processing zone includes a dual-channel mixing and splitting zone with a cross-shaped symmetrical layout, comprising a central confluence zone and mixing and splitting electrodes on both sides. The mixing and splitting electrodes are used to drive multicellular droplets to contact and fuse with diluent droplets to form reconstructed droplets, and to split the reconstructed droplets into two sub-droplets, allowing the cells in the reconstructed droplets to randomly rearrange between the two sub-droplets according to the liquid volume distribution.

[0060] The generation, transport, fusion, and splitting of the sample droplets are all achieved by applying controlled external forces to the droplets. In this invention, the controlled external force refers to an external force that can be adjusted within the digital microfluidic chip or microfluidic operating space to cause the droplets to undergo physical operations such as generation, transport, fusion, splitting, or path switching. The controlled external force can be applied by the driving module according to a preset driving sequence, and its target is the droplets located in different functional areas. By dividing the chip into droplet generation area, detection area, reconstruction processing area, collection area, and waste liquid area, and having the driving module control the driving unit corresponding to each area respectively, the droplets can complete continuous operations from generation, detection, splitting, reconstruction to reflux detection under the action of controlled external forces.

[0061] In a preferred embodiment, the controlled external force is an electric field. The driving module controls the electrode array on the digital microfluidic chip to cause the droplets to move in a controlled manner under dielectric wetting. Specifically, the driving module controls the meniscus electrode structure 1 in the droplet generation area to form and separate the cell suspension into sample droplets; controls the electrodes on the transport path to allow the sample droplets to enter the detection area; controls the electrodes in the path scheduling area to allow droplets corresponding to different recognition results to enter the collection area, waste liquid area, or reconstruction processing area respectively; and controls the mixing and splitting electrode in the reconstruction processing area to allow multi-cell droplets and auxiliary droplets to fuse to form reconstructed droplets, which further split into multiple sub-droplets. The formed sub-droplets return to the detection area for recognition and sorting, thereby realizing the recycling of multi-cell droplets and the extraction of single-cell droplets.

[0062] In other embodiments, the controlled external force can also be acoustic force, magnetic force, pressure, or a combination thereof. As long as it enables the droplets to perform operations such as generation, transport, fusion, splitting, and path switching, it can be used to realize the cyclic reconstruction sorting process of the present invention.

[0063] In step S1, the droplet volume is 1 nL–1 μL, preferably 5 nL–200 nL. The droplet generation frequency can be set according to the chip parallelism and driving timing; in a specific embodiment, it is 0.1–2 Hz, preferably 0.5 Hz. The consistency of the droplet volume is controlled by adjusting the electrode driving voltage and the action time.

[0064] The target cells in step S1 include one or more of the following: microalgal cells, yeast cells, bacterial cells, fungal spores, animal cells, or plant protoplasts.

[0065] The droplet reconstruction methods in step S4 include: directly splitting the multicellular droplet into two or more sub-droplets; The multicellular droplet is fused with at least one empty droplet before division; multiple divisions form multiple sub-droplets. By adding empty droplets for dilution, the number of cells in each sub-droplet can be reduced, increasing the probability of obtaining single-cell droplets again.

[0066] In step S4, the number of sub-droplets is determined based on the number of cells identified in the original droplet. For example, when the identified droplet contains 2 cells, it can split into 2 sub-droplets; when the identified droplet contains 4 cells, it can split into 4 sub-droplets, or it can first split into 2 sub-droplets and then continue to split.

[0067] S5: Retransmit the sub-droplets to the detection area and determine whether the preset termination condition has been met; if the preset termination condition has not been met, return to step S2 to continue execution; if the preset termination condition has been met, end the sorting process.

[0068] By dividing the electrode array into different functional areas and controlling the transport of droplets between these areas by the drive module, droplet generation, detection, fusion, and splitting are achieved. After reconstructing the multicellular droplets, they are returned to the detection area for cyclic sorting, thereby enabling the reuse of droplets and the efficient extraction of single cells.

[0069] The number of iterations in step S5 is determined based on at least one of the target recovery rate and a preset termination condition. For example, the cycle may terminate when the cumulative recovery rate of single-cell droplets reaches 80% or more; or the cycle may terminate when the number of iterations reaches 5.

[0070] The preset termination conditions include: reaching the preset number of cycles; the single-cell droplet recovery rate reaching the set threshold; the number of remaining droplets to be processed being lower than the set value; and the chip running time reaching the preset upper limit.

[0071] Under the condition of λ=1.0, the system automatically performs cyclic division on the identified multi-celled droplets. For droplets containing 2 cells, the maximum number of cycles is set to 6; for droplets containing 3 cells, the maximum number of cycles is set to 4.

[0072] The control system automatically schedules the droplet paths based on the detection results, so that single-cell droplets enter the collection area, multi-cell droplets enter the reconstruction area, and empty droplets enter the waste liquid area, thereby realizing dynamic load matching between the droplet generation, detection, reconstruction and collection modules and improving the overall processing throughput of the system.

[0073] Specifically, laser-etched transparent ITO glass is used as the electrode substrate, and SU-8 photoresist is used as the dielectric layer. SU-8 exhibits excellent dielectric properties and mechanical strength when its thickness is about 5 μm, for example, 3-10 μm, and also has good light transmittance.

[0074] like Figure 2 As shown, a meniscus-shaped droplet generation electrode 1 was designed. Compared to the traditional dumbbell-shaped structure, the meniscus design utilizes geometric constraints to induce droplet necking, achieving stable generation of 10 nL microdroplets with a feature size of 0.3 mm and a flow channel height of 60 μm. It exhibits a low coefficient of variation and can handle concentrations up to 10⁻⁶. 5 Cell suspension with cells / mL.

[0075] The arc-shaped cutting electrode is semi-circular and located at the front end of the main electrode, with a radius of 0.15-0.4 mm. The fixed cutting point structure is located between the arc-shaped cutting electrode and the next-stage electrode, with a fixed cutting point spacing of 0.1-0.3 mm. The electrode spacing 2 between the droplet manipulation electrode arrays is 20-50 micrometers, and the electrode linewidth 3 is 30-60 micrometers.

[0076] This invention does not directly discard multicellular droplets, but instead introduces them into a dual-channel mixing and splitting region. Based on a probabilistic allocation mechanism, such as a binomial distribution, the multicellular droplets fuse with the diluted droplets and then split again, with cells randomly assigned to the sub-droplets.

[0077] By repeating the process multiple times, such as 3-6 iterations, the initial multi-cell droplets can be deconstructed into single-cell droplets, significantly improving the single-cell recovery rate.

[0078] A queuing theory model is introduced to coordinate the load of the droplet generation, detection, and splitting modules. Calculations show that the system has an optimal operating point, such as λ≈1.0, at which point the system throughput is maximized and congestion does not occur.

[0079] In a preferred embodiment, before generating sample droplets containing cells to be sorted, the method further includes: determining a cell encapsulation expectation value based on the droplet generation rate and the splitting capacity of the remodeling processing zone, and adjusting the concentration of the cell suspension to be sorted based on the cell encapsulation expectation value so that the droplet processing load entering the remodeling processing zone matches its processing capacity.

[0080] To address the morphological differences among Haematococcus pluvialis, Micrococcus pluvialis, and Synechococcus, a lightweight recognition algorithm based on area, roundness, and two parameters was developed. Combining Hough transform and Canny edge detection, the single-frame processing time is <100ms, and the recognition accuracy is >95%.

[0081] The method of this invention, when operated with an average initial encapsulation concentration close to 1 cell / droplet, can significantly reduce sample loss caused by the discarding of multiple cell droplets. Through cyclic reconfiguration sorting, the total recovery rate of single-cell droplets is significantly improved compared to one-time sorting methods, and the system operates stably continuously, making it suitable for high-throughput automated screening applications.

[0082] A second aspect of the present invention discloses a single-cell droplet sorting system for the above-described method, such as... Figure 3 As shown, it includes: a digital microfluidic chip, a driving module, an image acquisition module, an image processing module, a controller, and a collection module. The digital microfluidic chip includes a droplet generation area, a detection area, a reconstruction processing area, a collection area, a waste liquid area, and a reflux path for returning sub-droplets to the detection area.

[0083] The driving module is used to drive the generation, transport, fusion, splitting and path switching of droplets on the digital microfluidic chip.

[0084] An image acquisition and recognition module is used to acquire images of droplets within the detection area and identify the number and / or type of cells within the droplets to obtain droplet recognition results. A controller is used to control the drive module to perform path scheduling based on the droplet recognition results. The path scheduling includes: guiding target single-cell droplets to a collection area; guiding empty droplets or non-target droplets to a waste liquid area; guiding multi-cell droplets to a reconstruction processing area; controlling the multi-cell droplets to fuse with at least one auxiliary droplet and split to form sub-droplets in the reconstruction processing area; and re-transporting the sub-droplets back to the detection area via the return path for re-identification and sorting. The collection module is used to collect target single-cell droplets.

[0085] According to one embodiment of the present invention, the digital microfluidic single-cell droplet sorting system of the present invention includes a digital microfluidic chip, a microscopic imaging module, and a hardware driving module. For example... Figure 4 As shown, the digital microfluidic chip adopts an upper and lower plate structure. The upper plate is a conductive transparent plate, and the lower plate is an array driving electrode plate. A droplet movement gap is formed between the two plates, with a preferred gap height of 50μm to 500μm. The lower electrode plate includes a droplet generation area, a detection area, a reconstruction processing area, a collection area (outlet A, outlet B, outlet C), and a waste outlet. The reconstruction processing area may include a mixing and splitting area for fusing and splitting multi-cell droplets with auxiliary droplets. The collection area is used to collect single-cell droplets, and the waste outlet is used to discharge droplets or droplets that do not meet the requirements. A hardware driving module is used to drive droplet generation, transport, mixing, and splitting, ensuring efficient droplet transmission and distribution within each area. A microscopic imaging module is used to monitor the droplet state in real time and to acquire and identify images of cells within the droplets to achieve accurate single-cell sorting. In this application, "area" refers to a functional division unit used to implement different processing functions.

[0086] The drive module controls electrodes in different functional areas to achieve droplet generation, transport, fusion, and splitting, while droplet classification is accomplished by image recognition results and path scheduling.

[0087] Transparent ITO conductive glass with a sheet resistance of 10 Ω / sq was selected as the substrate, and the electrode pattern was processed using an infrared laser etching machine. The droplet generation region adopted a meniscus design, with a feature dimension (dmin) of 0.3 mm at the end of the separating electrode and a lead width of 40 μm, enabling the generation of microdroplets of approximately 10 nL. For dielectric layer fabrication, SU-8 2005 photoresist was selected as the dielectric material. A dense dielectric layer with a thickness of approximately 5 μm was formed through spin coating (2000 rpm), soft baking, exposure, development, and hard baking (150℃, 30 min). Compared to silicon nitride (PECVD process) and PDMS, SU-8 offers lower fabrication costs and better light transmittance while maintaining insulation, and achieves effective contact angle modulation at a driving voltage of 80V. For channel height control, to ensure the stability of microdroplet generation, 60 μm diameter polystyrene microspheres mixed with UV adhesive were used as a support, and the channel height h between the upper and lower electrodes was strictly limited.

[0088] The microscopic imaging module may include an image acquisition module and an image processing module. The image acquisition module is used to acquire droplet images, and the image processing module is used to process the droplet images to identify the number or type of cells within the droplet.

[0089] The hardware configuration uses an inverted microscope in conjunction with a CMOS camera for image acquisition. An STM32 microcontroller can be used as the control unit, and an HV507 high-voltage driver chip can be used as the driving unit for electrode driving.

[0090] In one embodiment, the image processing procedure includes the following steps: preprocessing the acquired droplet image, for example, reducing noise through filtering; locating the droplet region in the image, for example, determining the droplet boundary based on shape detection or fitting methods; extracting target features, such as edge features or morphological features, within the droplet region and calculating relevant parameters to identify the number or type of cells within the droplet; performing Canny edge detection within the droplet mask region to extract cell contours and calculate their equivalent radius and roundness. In one implementation, the above process can be achieved using Gaussian filtering, Hough circle transform, RANSAC algorithm, and Canny edge detection.

[0091] This method has high sorting accuracy and cell recovery rate, can maintain high cell viability, and has high processing throughput.

[0092] In one embodiment, the sorting accuracy can reach more than 95%, and the processing time per frame can be less than about 100ms.

[0093] The controller may include a path scheduling unit and a drive timing control unit. The drive module is also used to control the movement, merging, and splitting of droplets between functional areas, while the path scheduling module is used to control the droplets to enter the collection area, waste liquid area, or reconstruction treatment area based on the identification results.

[0094] The path scheduling module is configured to: schedule the droplet to the collection area when the number of cells in the droplet is 1; schedule the droplet to the waste liquid area when the number of cells in the droplet is 0; and schedule the droplet to the reconstruction processing area when the number of cells in the droplet is greater than 1.

[0095] The reconstruction processing region includes a dual-channel hybrid splitting region, which includes a confluence region and hybrid splitting electrodes located on both sides of the confluence region.

[0096] The dual-channel mixing and splitting zone has a cross-shaped symmetrical layout, such as... Figure 4 As shown, the system includes a detection area in the center and hybrid splitting electrodes on both sides. The hybrid splitting electrodes are used to fuse multicellular droplets with diluted droplets, and then split them into two sub-droplets according to the binomial distribution principle to achieve random rearrangement of cells. The electrical connection unit uses a spring pin array to achieve a detachable electrical connection between the electrode pattern layer and the electric field drive control system.

[0097] Under controlled external force, the detected multi-celled droplets are not discarded directly. Instead, their redistribution conditions are first determined. If these conditions are met, they are mixed with empty droplets and undergo further division, allowing the cells to redistribute randomly into the sub-droplets. Single-celled droplets are directed to the target outlet, while empty droplets are directed to the waste liquid outlet. Through cyclic reconstruction and re-sorting of multi-celled droplets, droplet reuse is achieved, and single-cell recovery rate is improved. This transforms droplets deemed useless into useful droplets through cyclic reconstruction.

[0098] Figure 5 illustrates the mixed droplet splitting process and queuing theory parameter optimization of this invention. Figure 5(a) shows the mixed droplet splitting process, Figure 5(b) shows the system output single-cell droplet velocity under different parameter conditions, and Figure 5(c) shows the relationship between the average number of splitting events and the expected cell encapsulation value λ. Figure 5(a) shows the fusion, splitting, and redistribution process of multi-cell droplets and empty droplets in the mixed splitting region; Figure 5(b) shows the changing trends of system processing efficiency and recovery rate under different splitting times and different load conditions; Figure 5(c) shows the relationship between the average number of splitting events and the expected cell encapsulation value λ. Through the above analysis, the optimal operating range of the system can be determined, and load matching between the droplet generation module and the mixed splitting module can be achieved.

[0099] In one embodiment, in order to coordinate the processing capabilities among droplet generation, detection and splitting processes, system parameters can be analyzed and optimized based on a queuing theory model.

[0100] By establishing a model relating droplet generation rate to processing capacity, the processing load introduced by multicellular droplets can be estimated. Based on this, the system operating parameters are adjusted to avoid congestion under high throughput conditions.

[0101] In one embodiment, the droplet generation rate can be matched with the splitting processing capacity to keep the system in a stable operating state; when the generation rate is too high or the proportion of multi-cell droplets increases, the system may experience an increase in load, thereby affecting the overall throughput.

[0102] In one embodiment, the system can perform cyclic division processing on identified multi-cell droplets. The number of cycles can be set according to the number of cells in the droplet and can be adjusted according to actual needs to gradually reduce the proportion of multi-cell droplets and improve the recovery rate of single-cell droplets.

[0103] Experimental results show that by optimizing the parameters and using the above-mentioned cyclic processing strategy, the proportion of multi-cell droplets can be significantly reduced and the sorting efficiency of single cells can be improved.

[0104] Figure 6 This diagram illustrates the sorting performance evaluation of the present invention, showing the comparison of purity and recovery rate of different algal samples under single-sorting and iterative sorting conditions. The present invention evaluated the sorting performance of three types of samples: Haematococcus pluvialis, Chlorella vulgaris, and Synechococcus. The results show that, compared with the single-sorting strategy, the iterative sorting strategy significantly improves the recovery rate while maintaining high sorting purity under different cell encapsulation expectation conditions.

[0105] In one embodiment, a sorting experiment was conducted using a mixed suspension of various algal cells (e.g., Haematococcus pluvialis, Chlorella vulgaris, and Synechococcus).

[0106] In one embodiment, the cell concentration can be set to a certain range, for example, about 10^5 cells / mL, and the desired droplet encapsulation value is close to 1; the driving voltage and driving frequency can be set according to system requirements, for example, in the order of tens of volts and kilohertz respectively.

[0107] Experimental results show that this method can achieve high sorting purity, and the sorting purity of different algae can reach a high level. Among them, it has higher recognition and sorting accuracy for target objects with obvious characteristics.

[0108] Compared with single-sorting methods that do not employ a cyclic processing strategy, the method of the present invention can significantly improve the recovery rate of single-cell droplets and effectively reduce the proportion of multi-cell droplets.

[0109] In one embodiment, the sorted cells were cultured, and the results showed that their growth status was basically the same as that of the unsorted samples, indicating that the method has good biocompatibility.

[0110] The technical effects achieved by this invention are as follows: First, the detected multi-cell droplets are no longer directly discarded, but are transformed back into sub-droplets that can participate in sorting through droplet fusion, division and re-screening. This increases the probability of obtaining target single-cell droplets and reduces sample waste, and is especially suitable for screening scenarios of precious samples, low-abundance samples and high-value strains.

[0111] Secondly, existing droplet single-cell encapsulation methods are usually constrained by the Poisson distribution law, making it difficult to balance single-cell yield and purity. This invention improves the output efficiency of single-cell droplets without relying on extremely low encapsulation concentrations by performing subsequent reconstruction processing and iterative sorting on multi-cell droplets. This effectively reduces the proportion of colloidal droplets and multi-cell droplets.

[0112] Furthermore, droplet path scheduling is performed based on the detection results, and dynamic matching between droplet generation, detection, reconstruction and collection processes is achieved through a cyclic processing mechanism, thereby reducing droplet congestion, waiting and stagnation within the chip and improving system throughput and operational stability.

[0113] Finally, droplet-level manipulation based on digital microfluidic chips features low sample consumption, good sealing, and easy integration. It can be combined with microscopic imaging, image recognition, and automatic control systems to complete sorting while maintaining cell viability, making it suitable for screening, culturing, and analyzing various single-cell objects.

[0114] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0115] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for sorting single-cell droplets, characterized in that, By applying controlled external force to droplets, multi-cell droplets are fused and divided, then redistributed and returned to the detection area for cyclic sorting until a preset termination condition is met, thereby achieving droplet recycling and single-cell extraction, including: S1: Generate sample droplets containing cells to be sorted in the droplet generation area; S2: The sample droplet is transported to the detection area, the droplet is image acquired, and the number or type of cells in the droplet is identified; S3: Path control of the droplet is performed based on the recognition results, where: When the number of cells in the droplet is 1, the droplet is transported to the collection area. When the number of cells in the droplet is 0, the droplet is transported to the waste liquid area. When the number of cells in a droplet is greater than 1, the droplet is transported to the remodeling processing area. S4: In the reconstruction processing area, the droplets are driven by a driving control method to make the droplets fuse and split to form multiple sub-droplets, so that the multiple cells in the droplets are redistributed among the sub-droplets; S5: Retransmit the sub-droplets to the detection area and determine whether the preset termination condition has been met; if the preset termination condition has not been met, return to step S2 to continue execution; if the preset termination condition has been met, end the sorting process.

2. The method according to claim 1, characterized in that, The generation, transport, fusion, and splitting of the sample droplets are all achieved by applying controlled external forces to the droplets.

3. The method according to claim 2, characterized in that, The controlled external force includes electric force, acoustic force, magnetic force, pressure, or a combination thereof.

4. The method according to claim 3, characterized in that, The controlled external force is achieved by the electrowetting driving force generated by the electrode array on the digital microfluidic chip controlled by the drive module.

5. The method according to claim 4, characterized in that, In step S1, the droplet generation area generates the sample droplet by controlling the meniscus electrode structure through the driving module; the meniscus electrode structure includes a liquid storage electrode, a contraction channel, and a meniscus cutting area.

6. The method according to claim 1, characterized in that, The fusion process in step S4 includes fusing the droplet with at least one auxiliary droplet, the auxiliary droplet including a culture medium droplet, a buffer droplet, or a diluent droplet.

7. The method according to claim 1, characterized in that, The reconstruction processing area includes a dual-channel mixing and splitting area, which is used to fuse multi-cell droplets and auxiliary droplets to form a reconstructed droplet under the action of the driving module, and split the fused reconstructed droplet into two or more sub-droplets.

8. The method according to claim 1, characterized in that, The preset termination conditions include any one or a combination of the following: obtaining a preset number of single-cell droplets, reaching a preset number of iterations, the number of droplets to be processed being lower than a set threshold, or the single-cell recovery rate reaching a set value.

9. The method according to claim 1, characterized in that, Before generating sample droplets containing cells to be sorted, the process further includes: determining the cell encapsulation expectation value based on the droplet generation rate and the splitting capacity of the remodeling processing zone, and adjusting the concentration of the cell suspension to be sorted based on the cell encapsulation expectation value so that the droplet processing load entering the remodeling processing zone matches its processing capacity.

10. A single-cell droplet sorting system, characterized in that, include: Digital microfluidic chip, driver module, image acquisition module, image processing module, controller and collection module; The digital microfluidic chip includes a droplet generation area, a detection area, a reconstruction processing area, a collection area, a waste liquid area, and a reflux path for returning sub-droplets to the detection area. The driving module is used to drive the generation, transport, fusion, splitting and path switching of droplets on the digital microfluidic chip; The image acquisition and recognition module is used to acquire images of droplets within the detection area and identify the number and / or type of cells within the droplets to obtain droplet recognition results. The controller is used to control the drive module to perform path scheduling based on the droplet recognition result; The path scheduling includes: guiding the target single-cell droplet to the collection area; guiding empty droplets or non-target droplets to the waste liquid area; guiding multi-cell droplets to the reconstruction processing area; controlling the multi-cell droplets to fuse with at least one auxiliary droplet and split to form sub-droplets in the reconstruction processing area; and re-transporting the sub-droplets to the detection area via the return path for re-identification and sorting. The collection module is used to collect target single-cell droplets.

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