Methods for assaying biological cells in a microfluidic device
Methods are provided for the assay of secreted biomolecules using automated detection and characterization of micro-objects in a microfluidic device. The biomolecules can be secreted by cells, particularly immunological cells, such as T cells. The biomolecules being assayed can include cytokines, growth factors, and the like. Methods are also provided for assaying the cytotoxicity of a cell with respect to another, target cell. Also provided are kits and non-transitory computer-readable media in which programs are stored for causing a system comprising a computer to perform automated methods for detecting secreted biomolecules and/or cytotoxicity in a microfluidic device.
1. A method of assaying for antigen-specific cytotoxicity of a T lymphocyte (T cell) in a microfluidic device, the method comprising:
disposing the T cell within the microfluidic device; wherein the microfluidic device comprises a flow region for containing a flow of a first fluidic medium and a chamber opening to the flow region;
disposing a target cell in proximity to the T cell, wherein the target cell is configured to capture a secreted biomolecule from the T cell;
detecting the secreted biomolecule captured by the target cell after a period of exposure in proximity to the T cell by:
receiving a first image and one or more second images of a region of interest;
transforming each of the one or more second images to optically align with the first image;
processing pixel data in the first image using a machine learning algorithm to detect target cells present in the region of interest, the detection comprising identifying a boundary of each target cell; and
detecting a signal located within the boundary of each detected target cell in each of the one or more second images; and
determining the viability of the target cell after a period of exposure in proximity to the T cell.
2. The method of claim 1 , wherein the target cell expresses an antigen for which the T cell is specific.
3. The method of claim 1 , wherein the target cell is a cancer cell.
4. The method of claim 1 , wherein the chamber comprises a sequestration pen and the sequestration pen comprises an isolation region having a single opening and a connection region fluidically connecting the isolation region to the flow region, the isolation region being an unswept region of the microfluidic device.
5. The method of claim 1 , wherein the T cell and the target cell are each disposed in the chamber.
6. The method of claim 1 , wherein determining the viability of the target cell comprises contacting the target cell with a detectable marker configured to label a non-viable cell.
7. The method of claim 1 , further comprising labelling the T cell for the presence of one or more cell surface markers associated with proliferation, activation, metabolic activity, memory, exhaustion, and/or lineage.
8. A kit for assaying antigen-specific cytotoxicity by a T lymphocyte (T cell) in a microfluidic device, the kit comprising:
a microfluidic device comprising a flow region for containing a flow of a first fluidic medium and a chamber opening to the flow region; wherein the chamber comprises an isolation region having a single opening; and a connection region fluidically connecting the isolation region to the flow region; wherein the isolation region is an unswept region of the microfluidic device;
a cytotoxicity detection reagent configured to detect viability of a target cell; and
an image acquisition unit configured to detect a secreted biomolecule captured by the target cell when released from the T cell and:
receive a first image and one or more second images of a region of interest;
transform each of the one or more second images to optically align with the first image;
process pixel data in the first image using a machine learning algorithm to detect target cells present in the region of interest, the detection comprising identifying a boundary of each target cell; and
detect a signal located within the boundary of each detected target cell in each of the one or more second images.
9. The kit of claim 8 , wherein the cytotoxicity detection reagent comprises a reagent configured to label an apoptotic cell.
10. The kit of claim 8 , wherein the cytotoxicity detection reagent comprises a reagent configured to detect calcium flux or mitochondrial membrane potential.
11. The kit of claim 8 , further comprising a first capture object configured to capture a first secreted biomolecule of a T cell.
12. The method of claim 1 , wherein:
the first image is an illuminated image, and the second image is a non-illuminated image; or
the first image is a non-illuminated image, and the second image is an illuminated image or a non-illuminated image.
13. The method of claim 12 , wherein the non-illuminated image is a fluorescent image, an infrared image, or an ultraviolet image.
14. The method of claim 1 , wherein receiving the second image of the region of interest comprises receiving more than one second images, at least one of the more than one second images is a fluorescent image, and wherein each of the one or more second images is a fluorescent image.
15. The method of claim 13 , wherein each fluorescent image represents a fluorescent signal from a unique portion of the visible light spectrum, and wherein each fluorescent image represents fluorescent signal from a non-overlapping portion of the visible light spectrum.
16. The method of claim 1 , further comprising pre-processing the first image and the second image to reduce anomalies in the image data.
17. The method of claim 16 , wherein the pre-processing comprises reducing noise and/or optical distortion introduced during generation of the first image and the at least one second image.
18. The method of claim 1 , wherein determining a viability of the target cell further comprises processing pixel data in the first image using a machine learning algorithm and detecting the target cell present in the region of interest thereby identifying the boundary of the target cell.
19. The method of claim 18 , wherein processing pixel data in the first image comprises using the machine learning algorithm to generate a plurality of pixel masks from the first image for a corresponding plurality of target cell characteristics, and wherein each pixel mask comprises a set of pixel annotations, each pixel annotation of the set representing a probability that a corresponding pixel in the image represents the corresponding target cell characteristic.
20. The method of claim 19 , wherein the plurality of target cell characteristics comprises at least: (i) target cell center; (ii) target cell edge; and (iii) non-target cell.
21. The method of claim 20 , wherein detecting the target cell is based upon the pixel mask corresponding to the target cell center characteristic or a combination of pixel masks that includes the pixel mask corresponding to the target cell center characteristic.
22. The method of claim 1 , wherein disposing the T cell within the microfluidic device comprising disposing a single T cell into the chamber.
23. The method of claim 22 , wherein the microfluidic device further comprises a plurality of chambers and disposing the T cell within the microfluidic device comprises disposing a plurality of T cells within the microfluidic device and disposing a single T cell of the plurality of T cells into corresponding chamber of the plurality of chambers.