Highly Multiplexed Single Cell In Situ RNA Detection

Identifying the genetic basis of cellular function and identity has become a central question in understanding the functioning of complex biological systems in recent years. Single cell sequencing techniques have provided a great deal of insight into the transcriptional profiles of various cell types. However, single cell RNAseq studies require cells to be removed from their native environments resulting in the loss of spatial relationships between cells and suffer from low detection efficiency. Moving forward, a central question in further understanding large biological systems consisting of many disparate cell types will be how do these cells interact with each other to form functional tissues. To accomplish this goal, a method that keeps the tissue architecture intact is required. Single molecule fluorescence in situ hybridization (smFISH) is one such technique, but suffers from a lack of multiplex measurement capability as only a very few genes can be measured in any given sample and has low signal to noise ratio. Here I present a method that overcomes the low signal to noise ratio by using an amplification technique known as single molecule hybridization chain reaction (smHCR). smHCR coupled with the existing sequential FISH (seqFISH) method, which overcomes the inherent multiplexing limit of smFISH, provides a powerful tool to measure the copy numbers of 100’s of genes in single cell in situ. The mouse brain contains 100,000,000 cells arranged into distinct anatomical structures. While cell types have been previously characterized by morphology and electrophysiology, single cell RNA sequencing has recently identified many cell types based on gene expression profiles. On the other hand, the Allen Brain Atlas (ABA) provides a systematic gene expression database using in situ hybridization (ISH) of the entire mouse brain, but lacks the ability to correlate the expression of different genes in the same cell. Using the smHCR-seqFISH technique to measure the expression profiles of up to 249 genes in single cells in coronal brain sections, we have identified distinct cell clusters based on the expression profiles of 15000 cells and observed spatial patterning of cells in the hippocampus. In the dentate gyrus, we resolved lamina-layered patterns of cell clusters with a clear separation between the granule cell layer and the sub-granular zone. In CA1 and CA3, the data revealed distinct subregions, each with unique combinations of cell clusters. Particularly, we observed that the dorso-lateral CA1 is almost completely cellular homogeneous with increasing cellular heterogeneity on the dorsal to ventral axis. Together, these results demonstrate the power of highly multiplex in situ analysis to the brain, with further application to a wide range of biological systems.</p

Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles

It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample locations and their ordering in 1D. In this work, we extend the analytical bounds on the reconstruction error in such scenarios for quasi-bandlimited signals. We also prove that the method for such a reconstruction is resilient to a certain proportion of errors in the specification of the sample location ordering. We then express the problem of tomographic reconstruction of 2D images from 1D Radon projections under unknown angles with known angle distribution, as a special case for reconstruction of quasi-bandlimited signals from samples at unknown locations with known distribution. Building upon our theoretical background, we present asymptotic bounds for 2D quasi-bandlimited image reconstruction from 1D Radon projections in the unknown angles setting, which commonly occurs in cryo-electron microscopy (cryo-EM). To the best of our knowledge, this is the first piece of work to perform such an analysis for 2D cryo-EM, even though the associated reconstruction algorithms have been known for a long time

Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization

How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing- and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their surrounding environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states

Multiplexed Dynamic Imaging of Genomic Loci by Combined CRISPR Imaging and DNA Sequential FISH

Visualization of chromosome dynamics allows the investigation of spatiotemporal chromatin organization and its role in gene regulation and other cellular processes. However, current approaches to label multiple genomic loci in live cells have a fundamental limitation in the number of loci that can be labeled and uniquely identified. Here we describe an approach we call “track first and identify later” for multiplexed visualization of chromosome dynamics by combining two techniques: CRISPR imaging and DNA sequential fluorescence in situ hybridization. Our approach first labels and tracks chromosomal loci in live cells with the CRISPR-Cas9 system, then barcodes those loci by DNA sequential fluorescence in situ hybridization in fixed cells and resolves their identities. We demonstrate our approach by tracking telomere dynamics, identifying 12 unique subtelomeric regions with variable detection efficiencies, and tracking back the telomere dynamics of respective chromosomes in mouse embryonic stem cells

Sequence Polymorphism, Segmental Recombination and Toggling Amino Acid Residues within the DBL3X Domain of the VAR2CSA Placental Malaria Antigen

Plasmodium falciparum malaria remains one of the world's foremost health problems, primarily in highly endemic regions such as Sub-Saharan Africa, where it is responsible for substantial morbidity, mortality and economic losses. Malaria is a significant cause of severe disease and death in pregnant women and newborns, with pathogenesis being associated with expression of a unique variant of the multidomain Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) called VAR2CSA. Here, we characterize the polymorphism of the DBL3X domain of VAR2CSA and identify regions under selective pressure among placental parasites from women living in endemic western Kenya. In addition to significant levels of polymorphism, our analysis reveals evidence for diversification through intra-segmental recombination and novel mutations that likely contributed to the high number of unique VAR2CSA sequence types identified in this study. Interestingly, we also identified a number of critical residues that may be implicated in immune evasion through switching (or toggling) to alternative amino acids, including an arginine residue within the predicted binding pocket in subdomain III, which was previously implicated in binding to placental CSA. Overall, these findings are important for understanding parasite diversity in pregnant women and will be useful for identifying epitopes and variants of DBL3X to be included in a vaccine against placental malaria

In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus

Identifying the spatial organization of tissues at cellular resolution from single-cell gene expression profiles is essential to understanding biological systems. Using an in situ 3D multiplexed imaging method, seqFISH, we identify unique transcriptional states by quantifying and clustering up to 249 genes in 16,958 cells to examine whether the hippocampus is organized into transcriptionally distinct subregions. We identified distinct layers in the dentate gyrus corresponding to the granule cell layer and the subgranular zone and, contrary to previous reports, discovered that distinct subregions within the CA1 and CA3 are composed of unique combinations of cells in different transcriptional states. In addition, we found that the dorsal CA1 is relatively homogeneous at the single cell level, while ventral CA1 is highly heterogeneous. These structures and patterns are observed using different mice and different sets of genes. Together, these results demonstrate the power of seqFISH in transcriptional profiling of complex tissues

Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization

How intrinsic gene-regulatory networks interact with a cell's spatial environment to define its identity remains poorly understood. We developed an approach to distinguish between intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell type mapping combined with a hidden Markov random field model. We applied this approach to dissect the cell-type- and spatial-domain-associated heterogeneity in the mouse visual cortex region. Our analysis identified distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNA sequencing data, we identified previously unknown spatially associated subpopulations, which were validated by comparison with anatomical structures and Allen Brain Atlas images

Decomposing spatially dependent and cell type specific contributions to cellular heterogeneity

Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. Here we present a systematic approach to integrate both sequencing- and imaging-based single-cell transcriptomic profiles, thereby combining whole-transcriptomic and spatial information from these assays. We applied this approach to dissect the cell-type and spatial domain associated heterogeneity within the mouse visual cortex region. Our analysis identified distinct spatially associated signatures within glutamatergic and astrocyte cell compartments, indicating strong interactions between cells and their surrounding environment. Using these signatures as a guide to analyze single cell RNAseq data, we identified previously unknown, but spatially associated subpopulations. As such, our integrated approach provides a powerful tool for dissecting the roles of intrinsic regulatory networks and spatial environment in the maintenance of cellular states

Single-Cell Phenotyping within Transparent Intact Tissue through Whole-Body Clearing

Understanding the structure-function relationships at cellular, circuit, and organ-wide scale requires 3D anatomical and phenotypical maps, currently unavailable for many organs across species. At the root of this knowledge gap is the absence of a method that enables whole-organ imaging. Herein, we present techniques for tissue clearing in which whole organs and bodies are rendered macromolecule-permeable and optically transparent, thereby exposing their cellular structure with intact connectivity. We describe PACT (passive clarity technique), a protocol for passive tissue clearing and immunostaining of intact organs; RIMS (refractive index matching solution), a mounting media for imaging thick tissue; and PARS (perfusion-assisted agent release in situ), a method for whole-body clearing and immunolabeling. We show that in rodents PACT, RIMS, and PARS are compatible with endogenous-fluorescence, immunohistochemistry, RNA single-molecule FISH, long-term storage, and microscopy with cellular and subcellular resolution. These methods are applicable for high-resolution, high-content mapping and phenotyping of normal and pathological elements within intact organs and bodies