The quantification of immune cell subpopulations in blood is important for the diagnosis,prognosis and management of various diseases and medical conditions.Flow cytometry is currently the gold standard technique for ...The quantification of immune cell subpopulations in blood is important for the diagnosis,prognosis and management of various diseases and medical conditions.Flow cytometry is currently the gold standard technique for cell quantification;however,it is laborious,time-consuming and relies on bulky/expensive instrumentation,limiting its use to laboratories in high-resource settings.Microfluidic cytometers offering enhanced portability have been developed that are capable of rapid cell quantification;however,these platforms involve tedious sample preparation and processing protocols and/or require the use of specialized/expensive instrumentation for flow control and cell detection.Here,we report an artificial intelligence-enabled microfluidic cytometer for rapid CD4^(+)T cell quantification in whole blood requiring minimal sample preparation and instrumentation.CD4^(+)T cells in blood are labeled with anti-CD4 antibody-coated microbeads,which are driven through a microfluidic chip via gravity-driven slug flow,enabling pump-free operation.A video of the sample flowing in the chip is recorded using a microscope camera,which is analyzed using a convolutional neural network-based model that is trained to detect bead-labeled cells in the blood flow.The functionality of this platform was evaluated by analyzing fingerprick blood samples obtained from healthy donors,which revealed its ability to quantify CD4^(+)T cells with similar accuracy as flow cytometry(<10%deviation between both methods)while being at least 4×faster,less expensive,and simpler to operate.We envision that this platform can be readily modified to quantify other cell subpopulations in blood by using beads coated with different antibodies,making it a promising tool for performing cell count measurements outside of laboratories and in low-resource settings.展开更多
基金supported in part by the National Institutes of Health(R21CA283852)a Rice University COVID-19 Research Award(U50807).
文摘The quantification of immune cell subpopulations in blood is important for the diagnosis,prognosis and management of various diseases and medical conditions.Flow cytometry is currently the gold standard technique for cell quantification;however,it is laborious,time-consuming and relies on bulky/expensive instrumentation,limiting its use to laboratories in high-resource settings.Microfluidic cytometers offering enhanced portability have been developed that are capable of rapid cell quantification;however,these platforms involve tedious sample preparation and processing protocols and/or require the use of specialized/expensive instrumentation for flow control and cell detection.Here,we report an artificial intelligence-enabled microfluidic cytometer for rapid CD4^(+)T cell quantification in whole blood requiring minimal sample preparation and instrumentation.CD4^(+)T cells in blood are labeled with anti-CD4 antibody-coated microbeads,which are driven through a microfluidic chip via gravity-driven slug flow,enabling pump-free operation.A video of the sample flowing in the chip is recorded using a microscope camera,which is analyzed using a convolutional neural network-based model that is trained to detect bead-labeled cells in the blood flow.The functionality of this platform was evaluated by analyzing fingerprick blood samples obtained from healthy donors,which revealed its ability to quantify CD4^(+)T cells with similar accuracy as flow cytometry(<10%deviation between both methods)while being at least 4×faster,less expensive,and simpler to operate.We envision that this platform can be readily modified to quantify other cell subpopulations in blood by using beads coated with different antibodies,making it a promising tool for performing cell count measurements outside of laboratories and in low-resource settings.
