Applications of CyTOF in Brain Immune Component Studies

The brain is the most heterogeneous and complex tissue in the body.Previous studies have shown that immune cells are essential functional components in both healthy and pathological brains.Cytometry by the time of flight(CyTOF)is a high-dimensional single-cell detection technology that allows measurements of up to 100 cell markers with a small number of samples.This technique enables the identification and characterization of various cell types at the single-cell level under steady-state and diseased brain conditions.This review outlines three major advantages of the CyTOF technique compared with the traditional flow cytometry approach.We also discuss CyTOF applications in brain immune cell component research in both healthy and pathological brains.

McSNAC:A software to approximate first-order signaling networks from mass cytometry data

Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity.In particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes.However,computational analysis is required to reconstruct such networks with a mechanistic model.Methods:We propose our Mass cytometry Signaling Network Analysis Code(McSNAC),a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins.This assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear constructions.Furthermore,McSNAC may be limited to approximating indirect interactions between protein species,as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.Results:We carry out a series of in silico experiments here to show(1)McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system;(2)McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.Conclusions:These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Advanced technologies for single-cell in situ protein profiling

Cells are inherently heterogeneous to achieve a diverse spectrum of biological functions.To understand the underlying protein machinery that achieves these fascinating functions,it is important to develop advanced analytical methods that can profile proteins in their native environment,as protein expression,aggregation,degradation,and regulation define both normal physiology as well as pathogenicity.Genome and transcriptome sequencing have seen major advances at the single-cell levels,but comprehensive proteomic profiling is still challenging.The conventional proteomic methods,such as enzyme-linked immunosorbent assay(ELISA),western blot,and protein chips,can characterize biomarkers of interest.Still,these ensemble techniques are unsuitable for single-cell studies.Increasing evidence has shown the significance of in situ,sensitive,quantitative,and multiplexed profiling of biomarkers in single cells for diagnosis and treatment guidance.Here,we review the recent development of advanced imaging and spectroscopy techniques,including mass cytometry,immunofluorescence,and surfaceenhanced Raman spectrometry(SERS)for single-cell proteomic imaging.We also provide our view on the challenges and the outlook.