Systems and methods for particle analysis

Abstract

The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

1 .- 22 . (canceled) 
     
     
         23 . A method for assessing a subject's response to a therapeutic moiety, the method comprising
 (a) classifying in vitro, by a computer processor, a first cell based on one or more images of the first cell, wherein the one or more images of the first cell are obtained prior to subjecting the first cell to the therapeutic moiety;   (b) classifying in vitro, by the computer processor, a second cell based on one or more images of the second cell, wherein the one or more images of the second cell are obtained subsequent to subjecting the second cell to the therapeutic moiety; and   (c) determining efficacy of the therapeutic moiety for treatment of a condition of the subject based on the classifying in (a) and the classifying in (b).   
     
     
         24 . The method of  claim 23 , wherein the classifying based on the one or more images in (a) and the classifying based on the one or more images in (b) are sufficient to determine the efficacy. 
     
     
         25 . The method of  claim 23 , wherein, in (a), the classifying the first cell is based on a plurality of images from different angles of the first cell, and, in (b), the classifying the second cell is based on a plurality of images from different angles of the second cell. 
     
     
         26 . The method of  claim 23 , further comprising, in (a), subjecting the first cell to a flow cell to obtain the one or more images, and, in (b) subjecting the second cell to a flow cell to obtain the one or more images. 
     
     
         27 . The method of  claim 23 , wherein (i) the one or more images of the first cell are obtained and classified, by the computer processor, in real-time, and (ii) the one or more images of the second cell are obtained and classified, by the computer processor, in real-time. 
     
     
         28 . The method of  claim 23 , further comprising directing an imaging device to capture (i) the one or more images of the first cell and (ii) the one or more images of the second cell. 
     
     
         29 . The method of  claim 23 , wherein, the subjecting the second cell to the therapeutic moiety occurs in vitro. 
     
     
         30 . The method of  claim 23 , wherein, the subjecting the second cell to the therapeutic moiety occurs in vivo. 
     
     
         31 . The method of  claim 23 , wherein, the subjecting the second cell to the therapeutic moiety occurs in the subject. 
     
     
         32 . The method of  claim 23 , wherein the first cell and the second cell are derived from a biological sample of the subject. 
     
     
         33 . The method of  claim 32 , wherein the biological sample comprises blood or serum. 
     
     
         34 . The method of  claim 32 , wherein the biological sample comprises bodily fluid, breast, skin, eye, brain, liver, lung, kidney, prostate, ovary, spleen, lymph node, thyroid, pancreas, heart, skeletal muscle, intestine, larynx, esophagus, or stomach. 
     
     
         35 . The method of  claim 23 , further comprising selecting the therapeutic moiety for the treatment of the subject based on the efficacy. 
     
     
         36 . The method of  claim 23 , further comprising determining a unit dosage of the therapeutic moiety for the treatment of the subject based on the efficacy. 
     
     
         37 . The method of  claim 23 , wherein the classifying in (a) and the classifying in (b) are based on a deep learning algorithm. 
     
     
         38 . The method of  claim 23 , wherein the therapeutic moiety is an anti-cancer moiety. 
     
     
         39 . The method of  claim 23 , wherein the condition of the subject is a cancer. 
     
     
         40 . The method of  claim 39 , wherein the cancer is selected from the group consisting of lymphoma, myeloma, neuroblastoma, breast cancer, ovarian cancer, lung cancer, rhabdomyosarcoma, small-cell lung tumors, primary brain tumors, stomach cancer, colon cancer, pancreatic cancer, urinary bladder cancer, testicular cancer, lymphomas, thyroid cancer, neuroblastoma, esophageal cancer, genitourinary tract cancer, cervical cancer, endometrial cancer, adrenal cortical cancer, colon cancer, and prostate cancer. 
     
     
         41 . The method of  claim 23 , wherein (i) the one or more images of the first cell comprise a two-dimensional image, and (ii) the one or more images of the second cell comprise a two-dimensional image. 
     
     
         42 . The method of  claim 23 , wherein the efficacy is determined by determining a change in a release profile of a pro-inflammatory cytokine or an anti-inflammatory cytokine, between the first cell and the second cell.