Evaluating the Accuracy of Impedance Flow Cytometry with Cell-Sized Liposomes

Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

The natural history of small cell lung cancer (SCLC) includes rapid evolution from chemosensitivity to chemoresistance, although mechanisms underlying this evolution remain obscure due to scarcity of post-relapse tissue samples. We generated circulating tumor cell (CTC)-derived xenografts (CDXs) from SCLC patients to study intratumoral heterogeneity (ITH) via single-cell RNAseq of chemo-sensitive and -resistant CDXs and patient CTCs. We found globally increased ITH including heterogeneous expression of therapeutic targets and potential resistance pathways, such as EMT, between cellular subpopulations following treatment-resistance. Similarly, serial profiling of patient CTCs directly from blood confirmed increased ITH post-relapse. These data suggest that treatment-resistance in SCLC is characterized by coexisting subpopulations of cells with heterogeneous gene expression leading to multiple, concurrent resistance mechanisms. These findings emphasize the need for clinical efforts to focus on rational combination therapies for treatment-naïve SCLC tumors to maximize initial responses and counteract the emergence of ITH and diverse resistance mechanisms.