Automated detection and characterization of micro-objects in microfluidic devices
Methods are provided for the automated detection, characterization, and selection of micro-objects in a microfluidic device. In addition, methods are provided for grouping detected micro-objects into subgroups that share the same characteristics and, optionally, repositioning micro-objects in a selected sub-population within the microfluidic device. For example, micro-objects in a selected sub-population can be moved into sequestration pens. The methods also provide for visual displays of the micro-object characteristics, such as two- or three-dimensional graphs, and for user-based definition and/or selection of sub-populations of the detected micro-objects. In addition, non-transitory computer-readable medium in which a program is stored and systems for carrying out any of the disclosed methods are provided.
1. A method for detecting and characterizing micro-objects in a microfluidic device, the method comprising:
receiving a first image and one or more second images of a region of interest in the microfluidic device, the first image comprising an illuminated image and the one or more second images comprising one or more non-illuminated images;
transforming each of the one or more second images to optically align the one or more second images with the first image;
processing pixel data in the first image using a machine learning algorithm to detect micro-objects present in the region of interest, wherein detecting each micro-object comprises identifying a boundary of the micro-object; and
detecting a signal located within each boundary of each detected micro-object in each one of the one or more second images, wherein each detected signal is associated with a reagent that specifically binds to a biological molecule comprised by one or more of the detected micro-objects.
2. The method of claim 1 , wherein at least one of the one or more second images is a fluorescent image, and wherein the detected signal in the one or more second images is a fluorescent signal.
3. The method of claim 2 , wherein each fluorescent image represents fluorescent signal from a non-overlapping portion of the visible light spectrum.
4. The method of claim 1 , wherein each of the one or more second images is a fluorescent image, and wherein the detected signal in each of the one or more second images is a fluorescent signal.
5. The method of claim 1 , wherein processing pixel data in the first image to detect micro-objects present in the region of interest comprises using the machine learning algorithm to generate a plurality of pixel masks from the first image for a corresponding plurality of micro-object characteristics, 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 micro-object characteristic.
6. The method of claim 5 , wherein detecting the micro-objects comprises using a combination of pixel masks of the plurality of pixel masks.
7. The method of claim 5 , wherein the plurality of micro-object characteristics comprises at least: (i) micro-object center; (ii) micro-object edge; and (iii) non-micro-object.
8. The method of claim 7 , wherein detecting the micro-objects is based upon the pixel mask corresponding to the micro-object center characteristic or a combination of pixel masks that includes the pixel mask corresponding to the micro-object center characteristic.
9. The method of claim 1 , wherein there are at least two second images.
10. The method of claim 1 , further comprising:
grouping the detected micro-objects into sub-populations of micro-objects that share one or more of the same characteristics.
11. The method of claim 10 , wherein the detected micro-objects are grouped into sub-populations based upon their proximity in n-dimensional space, wherein each of the n dimensions is a measurable characteristic of the micro-objects.
12. The method of claim 1 , further comprising:
providing a visual display representing a distribution of at least one characteristic of the detected micro-objects.
13. The method of claim 12 , further comprising providing a user interface that allows the user to select a sub-population of the detected micro-objects and, optionally, to provide instruction(s) for repositioning the selected sub-population.
14. The method of claim 1 , further comprising increasing or decreasing the identified boundary of the micro-object.
15. The method of claim 1 , wherein the micro-objects are imaged within the microfluidic device, and wherein the pre-processing comprises subtracting out a repeating pattern produced by at least one component of the microfluidic device during imaging.
16. The method of claim 15 , wherein the pre-processing comprises applying a Fourier transform to the image to identify the repeating pattern.
17. The method of claim 15 , wherein the at least one component of the microfluidic device is a substrate surface.
18. The method of claim 15 , wherein the at least one component of the microfluidic device is a substrate surface including a photo-transistor array.
19. The method of claim 1 , wherein the micro-objects are living biological cells.
20. The method of claim 19 , wherein the living biological cells are: immunological cells; cells from a cell line or cancer cells; or oocytes, sperm, or embryos.
21. A system for automatically detecting micro-objects in a microfluidic device, comprising:
an image acquisition unit, comprising:
an imaging element configured to capture a first image and one or more second images of a region of interest in the microfluidic device, the first image comprising an illuminated image and the one or more second images comprising one or more non-illuminated images; and
an alignment engine configured to transform the second image to optically align the one or more second images with the first image, and
a micro-object detection and characterization unit communicatively connected to the image acquisition unit, comprising:
an image processing engine configured to process pixel data in the first image using a machine learning algorithm to detect micro-objects present in the region of interest, wherein detecting the micro-objects comprises identifying a boundary of each detected micro-object; and
a detection engine configured to detect a signal located within each boundary of each detected micro-object in each of the one or more second images, the first image comprising an illuminated image and the one or more second images comprising one or more non-illuminated images.
22. The system of claim 21 , further comprising:
a user interface, wherein the user interface is configured to allow the user to select a sub-population of the detected micro-objects and, optionally, to provide instruction(s) for repositioning the selected sub-population.