Abstract: A method for predicting a virtual crossmatch outcome using a machine learning human leukocyte antigen (HLA) antibody model includes receiving, as input, donor specific antibody (DSA) mean fluorescence intensity (MFI) values obtained from mixing donor-specific single antigen beads with serum samples of a prospective transplant recipient. The method further includes normalizing the DSA MFI values. The method further includes inputting the normalized DSA MFI values into the machine learning HLA antibody model. The method further includes receiving, as output from the HLA antibody model, a predicted crossmatch probability. The method further includes using the predicted crossmatch probability to inform a transplant decision.

Abstract: Provided are methods for rapid multiplex HLA genotyping and transcript quantitation. In some embodiments, the presently disclosed methods include providing a. sample from the cell, tissue, or organ that has mRNA derived from an HLA gene product; reverse transcribing the mRNA to produce a. pool of cDNAs; amplifying the cDNAs that result from reverse transcription of HLA-specific mRNAs to produce a pool of HLA-specific cDNAs; and determining the sequence of each HLA-specific cDNAs, whereby the HLA status of the cell, tissue, organ, or subject is determined. Also provided are methods for genotyping donor cells, tissues, and/or organs meant for transplantation into recipients. In some embodiments, the methods can be used to identify the presence of absence of each of HLA-A, -B, -C, -DRB 1/3/4/5, -DQA1, and -DQB1 in a biological sample.