Massively Parallel Rapid Single Cell Reader and Sorter
20210245159 · 2021-08-12
Assignee
Inventors
Cpc classification
B01L2200/0652
PERFORMING OPERATIONS; TRANSPORTING
B01L2400/0454
PERFORMING OPERATIONS; TRANSPORTING
B01L3/502761
PERFORMING OPERATIONS; TRANSPORTING
International classification
Abstract
Current state of the art in microfluidic pumping, single cell labelling, mixing, emulsification, incubation, optical excitation, reading and sorting component technology is presented. This is followed by the description of the invention, that fuses these components into a single system, available in four configurations, characterised by three structural and functional innovations. The first unique feature is intra and inter parallel architecture enabling fast, high-throughput, flexible and scalable single cell reading and sorting. Second—dual, laser-enabled detection of information, both fluorescent-genetic and visual-morphological; and its combination and processing using machine learning algorithms. Third—novel mixing, incubation and reading-sorting component structure.
Claims
1. Massively parallel rapid single cell reading and sorting device, characterised by four configurations, comprising pumping, microfluidic emulsification, incubation and reading-sorting modules.
2. Device as in claim 1, wherein the key property of said modules is internal and external parallelism.
3. Device as in claim 1, wherein the main material of said modules is either poly-dimethylsiloxane or the more robust borosilicate glass or quartz.
4. Device as in claim 1, wherein the said emulsification module encapsulates single cells into droplets and performs their fluorescent marking.
5. Device as in claim 1, wherein the said microfluidic-thermoelectric incubation module consists of serpentine channels and Peltier plates underneath.
6. Device as in claim 1, wherein the said reading-sorting module consists of: (i) continuous wave laser and single cell fluorescent genetic sequence sensor, (ii) titanium-sapphire pulsed laser and single cell visual-morphological information sensor, (iii) data processing unit, based on deep neural nets, classification and hierarchical and k-means clustering.
7. Device as in claim 1, wherein the said reading-sorting module uses cell type output information from (iii) inside (iv)—an asymmetric herringbone microfluidic-dielectrophoretic or microfluidic-optoelectronic construction, enabling multi-channel sorted cell output.
8. Massively parallel rapid single cell reading and sorting device is used for therapeutic antibody selection and engineering, cancer cell sorting, stem, progenitor and rare cell isolation, cell sorting according to genotype and phenotype, genotype-phenotype mapping, genomic library development and directed enzyme evolution.
Description
[0011] Below individual modules and their configurations are described: (P) pumping, (E) emulsification, (I) incubation and (R) reading and sorting.
[0012] P) There are two types of pumping modules: P.sub.k+1 ir P.sub.k+1:n. Different configurations of the device require different pumps. P.sub.k+1 (also labelled as PS.sub.k+1 to emphasize presence of the sample) is used in conf. A (
[0013] Drawings also use letters S—denoting the sample (in configurations B and D) and J—denoting junction (config. A and C).
[0014] E) E.sub.n denotes the emulsification module, which consists of smaller e.sub.m functional units.
[0015] I) Droplets then travel along m serpentine channels of the incubation module I.sub.n. This geometry induces mixing and allows incubation time control. A Peltier plate adds further—thermoelectric—reaction control.
[0016] R) In the reading and sorting module R.sub.n continuous wave laser (
[0017] The user has the option to pre-set cell types he would like to capture. He also has a free-style setting, where the system scans and collects information about potentially thousands of cell types present in the sample; the DPU then clusters them by similarity into categories. Here, depending on user settings, the DPU utilises another family of machine learning algorithms: k-means and hierarchical clustering. A noteworthy setting is sorting cells by similarity into up to 98 categories; this matches the No of wells in a standard microplate. This paves the way for precision genomic library creation; or physical sorting of cells without the need to pre-define categories—automatically; according to feature similarity.
[0018] These three pillars: (i) fluorescent-genetic information collection, (ii) visual-morphological information collection and (iii) processing of said information in the DPU using machine learning algorithms form the second key innovation of the device.
[0019] Physical sorting takes place in the asymmetric herringbone microfluidic-dielectrophoretic (
[0020] Cells exit the side channels (
[0021] The device has four configurations. In the first (
[0022] In addition to high throughput and data quality commercial success also hinges on the use of a simple operational protocol. For this reason, in configurations A and B (
[0023] It is understood that the following claims are intended to cover all of the generic and specific features of the invention herein described and all statements of the scope of the invention which, as a matter of language, might be said to fall therebetween.