Abstract: Devices and methods for label-free sensing of lymphocyte activation and identify are disclosed. The activation status of B cells and NK cells can be reliably determined. A general classifier capable of determining the activation status of lymphocytes having unknown identity (i.e., unknown whether a T cell, B cell, or NK cell) is disclosed. An identity classifier capable of differentiating T cells from B cells from NK cells is disclosed. A six-class classifier is disclosed which is capable of identifying both lymphocyte identity and activation status.

Abstract: The present disclosure provides a new sensing device and method for determining the activation status of neutrophils. The device has an autofluorescence spectrometer and a cell analysis observation zone. A current activation status of the neutrophil cell is identified based on a current activation prediction, where the current activation prediction is computed using at least a portion of the autofluorescence data set. The current activation prediction is computed using at least two metabolic endpoints of the autofluorescence data set and at least one morphological parameter as an input. The at least two metabolic endpoints include either: reduced nicotinamide adenine dinucleotide and/or reduced nicotinamide dinucleotide phosphate (NAD(P)H) mean fluorescence lifetime (?m), NAD(P)H shortest fluorescence amplitude component (?1), and NAD(P)H shortest fluorescence lifetime component (?1); or the NAD(P)H ?m and flavin adenine dinucleotide (FAD) ?m.

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.