RNA proximity sequencing data and analysis pipeline from a human neuroblastoma nuclear transcriptome.

We have previously developed and described a method for measuring RNA co-locations within cells, called Proximity RNA-seq, which promises insights into RNA expression, processing, storage and translation. Here, we describe transcriptome-wide proximity RNA-seq datasets obtained from human neuroblastoma SH-SY5Y cell nuclei. To aid future users of this method, we also describe and release our analysis pipeline, CloseCall, which maps cDNA to a custom transcript annotation and allocates cDNA-linked barcodes to barcode groups. CloseCall then performs Monte Carlo simulations on the data to identify pairs of transcripts, which are co-barcoded more frequently than expected by chance. Furthermore, derived co-barcoding frequencies for individual transcripts, dubbed valency, serve as proxies for RNA density or connectivity for that given transcript. We outline how this pipeline was applied to these sequencing datasets and openly share the processed data outputs and access to a virtual machine that runs CloseCall. The resulting data specify the spatial organization of RNAs and builds hypotheses for potential regulatory relationships between RNAs

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies.

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states

Emerging concepts and tools in cell mechanomemory

Studying a cell’s ability to sense and respond to mechanical cues has emerged as a field unto itself over the last several decades, and this research area is now populated by engineers and biologists alike. As just one example of this cell mechanosensing, fibroblasts on soft substrates have slower growth rates, smaller spread areas, lower traction forces, and slower migration speeds compared to cells on stiff substrates. This phenomenon is not unique to fibroblasts, as these behaviors, and others, on soft substrates has been shown across a variety of cell types, and reproduced in many different labs. Thus far, the field has focused on discerning the mechanisms of cell mechanosensing through ion channels, focal adhesions and integrin-binding sites to the ECM, and the cell cytoskeleton. A relatively new concept in the field is that of mechanical memory, which refers to persistent effects of mechanical stimuli long after they have been removed from said stimulus. Here, we review this literature, provide an overview of emerging substrate fabrication approaches likely to be helpful for the field, and suggest the adaption of genetic tools for studying mechanical memory

Protein nanobarcodes enable single-step multiplexed fluorescence imaging

Multiplexed cellular imaging typically relies on the sequential application of detection probes, as antibodies or DNA barcodes, which is complex and time-consuming. To address this, we developed here protein nanobarcodes, composed of combinations of epitopes recognized by specific sets of nanobodies. The nanobarcodes are read in a single imaging step, relying on nanobodies conjugated to distinct fluorophores, which enables a precise analysis of large numbers of protein combinations. Fluorescence images from nanobarcodes were used as input images for a deep neural network, which was able to identify proteins with high precision. We thus present an efficient and straightforward protein identification method, which is applicable to relatively complex biological assays. We demonstrate this by a multicell competition assay, in which we successfully used our nanobarcoded proteins together with neurexin and neuroligin isoforms, thereby testing the preferred binding combinations of multiple isoforms, in parallel

Elucidation of the Roles of Tumor Integrin 1 in the Extravasation Stage of the Metastasis Cascade

Tumor integrin β1 (ITGB1) contributes to primary tumor growth and metastasis, but its specific roles in extravasation have not yet been clearly elucidated. In this study, we engineered a three-dimensional microfluidic model of the human microvasculature to recapitulate the environment wherein extravasation takes place and assess the consequences of β1 depletion in cancer cells. Combined with confocal imaging, these tools allowed us to decipher the detailed morphology of transmigrating tumor cells and associated endothelial cells in vitro at high spatio-temporal resolution not easily achieved in conventional transmigration assays. Dynamic imaging revealed that β1-depleted cells lacked the ability to sustain protrusions into the subendothelial matrix in contrast with control cells. Specifically, adhesion via α3β1 and α6β1 to subendothelial laminin was a critical prerequisite for successful transmigration. β1 was required to invade past the endothelial basement membrane, whereas its attenuation in a syngeneic tumor model resulted in reduced metastatic colonization of the lung, an effect not observed upon depletion of other integrin alpha and beta subunits. Collectively, our findings in this novel model of the extravasation microenvironment revealed a critical requirement for β1 in several steps of extravasation, providing new insights into the mechanisms underlying metastasis.National Cancer Institute (U.S.) (Grant 1U01CA202177-01

Integrated spatial genomics reveals global architecture of single nuclei

Identifying the relationships between chromosome structures, nuclear bodies, chromatin states and gene expression is an overarching goal of nuclear-organization studies. Because individual cells appear to be highly variable at all these levels, it is essential to map different modalities in the same cells. Here we report the imaging of 3,660 chromosomal loci in single mouse embryonic stem (ES) cells using DNA seqFISH+, along with 17 chromatin marks and subnuclear structures by sequential immunofluorescence and the expression profile of 70 RNAs. Many loci were invariably associated with immunofluorescence marks in single mouse ES cells. These loci form ‘fixed points’ in the nuclear organizations of single cells and often appear on the surfaces of nuclear bodies and zones defined by combinatorial chromatin marks. Furthermore, highly expressed genes appear to be pre-positioned to active nuclear zones, independent of bursting dynamics in single cells. Our analysis also uncovered several distinct mouse ES cell subpopulations with characteristic combinatorial chromatin states. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3 trimethylation at lysine 27 (H3K27me3) and macroH2A1 (mH2A1), are heritable over at least 3–4 generations, whereas other marks fluctuate on a faster time scale. This seqFISH+-based spatial multimodal approach can be used to explore nuclear organization and cell states in diverse biological systems