Colon cancer is a deadly disease affecting millions of people worldwide. Current treatment challenges include management of disease burden as well as improvements in detection and targeting of tumor cells. To identify disease state-specific surface antigen signatures, we combined fluorescent cell barcoding with high-throughput flow cytometric profiling of primary and metastatic colon cancer lines (SW480, SW620, and HCT116). Our multiplexed technique offers improvements over conventional methods by permitting the simultaneous and rapid screening of cancer cells with reduced effort and cost. The method uses a protein-level analysis with commercially available antibodies on live cells with intact epitopes to detect potential tumor-specific targets that can be further investigated for their clinical utility. Multiplexed antibody arrays can easily be applied to other tumor types or pathologies for discovery-based approaches to target identification.

By including oligonucleotide-labeled antibodies into high-throughput single-cell RNA-sequencing protocols, combined transcript and protein expression data can be acquired on the single-cell level. Here, we describe a protocol for the combined analysis of over 40 proteins and 400 genes on over 104 cells using the nano-well based Rhapsody platform. We also include a workflow for sample multiplexing, which uniquely identifies the initial source of cells (such as tissue type or donor) in the downstream analysis after upstream pooling. For complete information on the use and execution of this protocol, please refer to Mair et al. (2020).

Neural induction of human pluripotent stem cells often yields heterogeneous cell populations that can hamper quantitative and comparative analyses. There is a need for improved differentiation and enrichment procedures that generate highly pure populations of neural stem cells (NSC), glia and neurons. One way to address this problem is to identify cell-surface signatures that enable the isolation of these cell types from heterogeneous cell populations by fluorescence activated cell sorting (FACS).

The actin-binding protein Kelch-like 1 (KLHL1) can modulate voltage-gated calcium channels in vitro. KLHL1 interacts with actin and with the pore-forming subunits of Cav2.1 and CaV3.2 calcium channels, resulting in up-regulation of P/Q and T-type current density. Here we tested whether endogenous KLHL1 modulates voltage gated calcium currents in cultured hippocampal neurons by down-regulating the expression of KLHL1 via adenoviral delivery of shRNA targeted against KLHL1 (shKLHL1). Control adenoviruses did not affect any of the neuronal properties measured, yet down-regulation of KLHL1 resulted in HVA current densities ~68% smaller and LVA current densities 44% smaller than uninfected controls, with a concomitant reduction in α(1A) and α(1H) protein levels. Biophysical analysis and western blot experiments suggest Ca(V)3.1 and 3.3 currents are also present in shKLHL1-infected neurons. Synapsin I levels, miniature postsynaptic current frequency, and excitatory and inhibitory synapse number were reduced in KLHL1 knockdown. This study corroborates the physiological role of KLHL1 as a calcium channel modulator and demonstrates a novel, presynaptic role.

Stem cells reside in niches that regulate the balance between self-renewal and differentiation. The identity of a stem cell is linked with the ability to interact with its niche through adhesion mechanisms. To identify targets that disrupt cancer stem cell (CSC) adhesion, we performed a flow cytometry screen on patient-derived glioblastoma (GBM) cells and identified junctional adhesion molecule A (JAM-A) as a CSC adhesion mechanism essential for self-renewal and tumor growth. JAM-A was dispensable for normal neural stem/progenitor cell (NPC) function, and JAM-A expression was reduced in normal brain versus GBM. Targeting JAM-A compromised the self-renewal of CSCs. JAM-A expression negatively correlated to GBM patient prognosis. Our results demonstrate that GBM-targeting strategies can be identified through screening adhesion receptors and JAM-A represents a mechanism for niche-driven CSC maintenance.

High-throughput single-cell RNA sequencing (scRNA-seq) has become a frequently used tool to assess immune cell heterogeneity. Recently, the combined measurement of RNA and protein expression was developed, commonly known as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcripts but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a targeted transcriptomics approach that combines an analysis of over 400 genes with simultaneous measurement of over 40 proteins on 2 × 104 cells in a single experiment. This targeted approach requires only about one-tenth of the read depth compared to a whole-transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic datasets, we adapted one-dimensional soli expression by nonlinear stochastic embedding (One-SENSE) for intuitive visualization of protein-transcript relationships on a single-cell level.