Abstract: Systems and methods for identifying a current reprogramming status and for predicting a future reprogramming status for reprogramming intermediate cells (i.e., somatic cells undergoing reprogramming) are provided. Label-free autofluorescence measurements are combined with machine learning techniques to provide highly accurate identification of current reprogramming status and prediction of future reprogramming status. The identification of current reprogramming status utilizes metabolic endpoints from the autofluorescence data set. The prediction of future reprogramming status utilizes a pseudotime line constructed from autofluorescence data of reprogramming intermediate cells having a known reprogramming status.

Abstract: Systems and methods for classifying and/or sorting T cells by activation state are disclosed. The system includes a cell classifying pathway, a single-cell autofluorescence image sensor, a processor, and a non-transitory computer-readable memory. The memory is accessible to the processor and has stored thereon a trained convolutional neural network and instructions. The instructions, when executed by the processor, cause the processor to: a) receive the autofluorescence intensity image; b) optionally pre-process the autofluorescence intensity image to produce an adjusted autofluorescence intensity image; c) input the autofluorescence intensity image or the adjusted autofluorescence intensity image into the trained convolutional neural network to produce an activation prediction for the T cell.

Abstract: Systems and methods for classifying T cells by activation state are disclosed. The system includes a cell analysis pathway, a time-resolved autofluorescence decay spectrometer, a processor, and a non-transitory computer-readable memory. The memory is accessible to the processor and has stored thereon instructions. The instructions, when executed by the processor, cause the processor to: a) receive the time-resolved autofluorescence decay signal; b) compute at least a first phasor coordinate at a first frequency and a second phasor coordinate at a second frequency from the time-resolved autofluorescence decay signal, wherein the first and second frequency are different; and c) compute an activation prediction for the T cell using at least the first phasor coordinate and the second phasor coordinate.

Abstract: Systems and methods for classifying and/or sorting T cells by activation state are disclosed. The system includes a cell classifying pathway, a single-cell autofluorescence image sensor, a processor, and a non-transitory computer-readable memory. The memory is accessible to the processor and has stored thereon a trained convolutional neural network and instructions. The instructions, when executed by the processor, cause the processor to: a) receive the autofluorescence intensity image; b) optionally pre-process the autofluorescence intensity image to produce an adjusted autofluorescence intensity image; c) input the autofluorescence intensity image or the adjusted autofluorescence intensity image into the trained convolutional neural network to produce an activation prediction for the T cell.