Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH.

Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells1-5. Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells-with high accuracy and sub-diffraction-limit resolution-in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand-receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ.

BORIS promotes chromatin regulatory interactions in treatment-resistant cancer cells.

The CCCTC-binding factor (CTCF), which anchors DNA loops that organize the genome into structural domains, has a central role in gene control by facilitating or constraining interactions between genes and their regulatory elements1,2. In cancer cells, the disruption of CTCF binding at specific loci by somatic mutation3,4 or DNA hypermethylation5 results in the loss of loop anchors and consequent activation of oncogenes. By contrast, the germ-cell-specific paralogue of CTCF, BORIS (brother of the regulator of imprinted sites, also known as CTCFL)6, is overexpressed in several cancers7-9, but its contributions to the malignant phenotype remain unclear. Here we show that aberrant upregulation of BORIS promotes chromatin interactions in ALK-mutated, MYCN-amplified neuroblastoma10 cells that develop resistance to ALK inhibition. These cells are reprogrammed to a distinct phenotypic state during the acquisition of resistance, a process defined by the initial loss of MYCN expression followed by subsequent overexpression of BORIS and a concomitant switch in cellular dependence from MYCN to BORIS. The resultant BORIS-regulated alterations in chromatin looping lead to the formation of super-enhancers that drive the ectopic expression of a subset of proneural transcription factors that ultimately define the resistance phenotype. These results identify a previously unrecognized role of BORIS-to promote regulatory chromatin interactions that support specific cancer phenotypes.

Advances in spatial transcriptomic data analysis.

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

Multifaceted actions of Zeb2 in postnatal neurogenesis from the ventricular-subventricular zone to the olfactory bulb.

The transcription factor Zeb2 controls fate specification and subsequent differentiation and maturation of multiple cell types in various embryonic tissues. It binds many protein partners, including activated Smad proteins and the NuRD co-repressor complex. How Zeb2 subdomains support cell differentiation in various contexts has remained elusive. Here, we studied the role of Zeb2 and its domains in neurogenesis and neural differentiation in the young postnatal ventricular-subventricular zone (V-SVZ), in which neural stem cells generate olfactory bulb-destined interneurons. Conditional Zeb2 knockouts and separate acute loss- and gain-of-function approaches indicated that Zeb2 is essential for controlling apoptosis and neuronal differentiation of V-SVZ progenitors before and after birth, and we identified Sox6 as a potential downstream target gene of Zeb2. Zeb2 genetic inactivation impaired the differentiation potential of the V-SVZ niche in a cell-autonomous fashion. We also provide evidence that its normal function in the V-SVZ also involves non-autonomous mechanisms. Additionally, we demonstrate distinct roles for Zeb2 protein-binding domains, suggesting that Zeb2 partners co-determine neuronal output from the mouse V-SVZ in both quantitative and qualitative ways in early postnatal life.

Discovery and resistance mechanism of a selective CDK12 degrader.

Cyclin-dependent kinase 12 (CDK12) is an emerging therapeutic target due to its role in regulating transcription of DNA-damage response (DDR) genes. However, development of selective small molecules targeting CDK12 has been challenging due to the high degree of homology between kinase domains of CDK12 and other transcriptional CDKs, most notably CDK13. In the present study, we report the rational design and characterization of a CDK12-specific degrader, BSJ-4-116. BSJ-4-116 selectively degraded CDK12 as assessed through quantitative proteomics. Selective degradation of CDK12 resulted in premature cleavage and poly(adenylation) of DDR genes. Moreover, BSJ-4-116 exhibited potent antiproliferative effects, alone and in combination with the poly(ADP-ribose) polymerase inhibitor olaparib, as well as when used as a single agent against cell lines resistant to covalent CDK12 inhibitors. Two point mutations in CDK12 were identified that confer resistance to BSJ-4-116, demonstrating a potential mechanism that tumor cells can use to evade bivalent degrader molecules.

Stem cells, cell therapies, and bioengineering in lung biology and disease 2021.

The 9th biennial conference titled "Stem Cells, Cell Therapies, and Bioengineering in Lung Biology and Diseases" was hosted virtually, due to the ongoing COVID-19 pandemic, in collaboration with the University of Vermont Larner College of Medicine, the National Heart, Lung, and Blood Institute, the Alpha-1 Foundation, the Cystic Fibrosis Foundation, and the International Society for Cell & Gene Therapy. The event was held from July 12th through 15th, 2021 with a pre-conference workshop held on July 9th. As in previous years, the objectives remained to review and discuss the status of active research areas involving stem cells (SCs), cellular therapeutics, and bioengineering as they relate to the human lung. Topics included 1) technological advancements in the in situ analysis of lung tissues, 2) new insights into stem cell signaling and plasticity in lung remodeling and regeneration, 3) the impact of extracellular matrix in stem cell regulation and airway engineering in lung regeneration, 4) differentiating and delivering stem cell therapeutics to the lung, 5) regeneration in response to viral infection, and 6) ethical development of cell-based treatments for lung diseases. This selection of topics represents some of the most dynamic and current research areas in lung biology. The virtual workshop included active discussion on state-of-the-art methods relating to the core features of the 2021 conference, including in situ proteomics, lung-on-chip, induced pluripotent stem cell (iPSC)-airway differentiation, and light sheet microscopy. The conference concluded with an open discussion to suggest funding priorities and recommendations for future research directions in basic and translational lung biology.

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFß/BMP system components in transition from embryonic stem cells to neural progenitors.

Cooperative actions of extrinsic signals and cell-intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type ß (TGFß) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell-type specific and context-dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFß system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA-expression dynamics, gene-gene interactions, and single-cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA-based perturbation of each individual component and documented the effect on steady-state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene-gene interactions display a marked cell-stage specific behavior. Furthermore, single-cell RNA-profiling at individual stages demonstrated the presence of detailed co-expression modules and subpopulations showing stable co-expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions.

RESCUE: imputing dropout events in single-cell RNA-sequencing data.

BACKGROUND: Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved. RESULTS: Here we present a computational method, called RESCUE, to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns. Unlike existing methods, we use an ensemble-based approach to minimize the feature selection bias on imputation. By comparative analysis of simulated and real single-cell RNA-seq datasets, we show that RESCUE outperforms existing methods in terms of imputation accuracy which leads to more precise cell-type identification. CONCLUSIONS: Taken together, these results suggest that RESCUE is a useful tool for mitigating dropouts in single-cell RNA-seq data. RESCUE is implemented in R and available at https://github.com/seasamgo/rescue .

Zeb2 Regulates Cell Fate at the Exit from Epiblast State in Mouse Embryonic Stem Cells.

In human embryonic stem cells (ESCs) the transcription factor Zeb2 regulates neuroectoderm versus mesendoderm formation, but it is unclear how Zeb2 affects the global transcriptional regulatory network in these cell-fate decisions. We generated Zeb2 knockout (KO) mouse ESCs, subjected them as embryoid bodies (EBs) to neural and general differentiation and carried out temporal RNA-sequencing (RNA-seq) and reduced representation bisulfite sequencing (RRBS) analysis in neural differentiation. This shows that Zeb2 acts preferentially as a transcriptional repressor associated with developmental progression and that Zeb2 KO ESCs can exit from their naïve state. However, most cells in these EBs stall in an early epiblast-like state and are impaired in both neural and mesendodermal differentiation. Genes involved in pluripotency, epithelial-to-mesenchymal transition (EMT), and DNA-(de)methylation, including Tet1, are deregulated in the absence of Zeb2. The observed elevated Tet1 levels in the mutant cells and the knowledge of previously mapped Tet1-binding sites correlate with loss-of-methylation in neural-stimulating conditions, however, after the cells initially acquired the correct DNA-methyl marks. Interestingly, cells from such Zeb2 KO EBs maintain the ability to re-adapt to 2i + LIF conditions even after prolonged differentiation, while knockdown of Tet1 partially rescues their impaired differentiation. Hence, in addition to its role in EMT, Zeb2 is critical in ESCs for exit from the epiblast state, and links the pluripotency network and DNA-methylation with irreversible commitment to differentiation. Stem Cells 2017;35:611-625.

The MYCN 5' UTR as a therapeutic target in neuroblastoma.

Tumor MYCN amplification is seen in high-risk neuroblastoma, yet direct targeting of this oncogenic transcription factor has been challenging. Here, we take advantage of the dependence of MYCN-amplified neuroblastoma cells on increased protein synthesis to inhibit the activity of eukaryotic translation initiation factor 4A1 (eIF4A1) using an amidino-rocaglate, CMLD012824. Consistent with the role of this RNA helicase in resolving structural barriers in 5' untranslated regions (UTRs), CMLD012824 increased eIF4A1 affinity for polypurine-rich 5' UTRs, including that of the MYCN and associated transcripts with critical roles in cell proliferation. CMLD012824-mediated clamping of eIF4A1 spanned the full lengths of mRNAs, while translational inhibition was mediated through 5' UTR binding in a cap-dependent and -independent manner. Finally, CMLD012824 led to growth inhibition in MYCN-amplified neuroblastoma models without generalized toxicity. Our studies highlight the key role of eIF4A1 in MYCN-amplified neuroblastoma and demonstrate the therapeutic potential of disrupting its function.

MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data.

Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.

Aberrant activation of wound healing programs within the metastatic niche facilitates lung colonization by osteosarcoma cells.

PURPOSE: Lung metastasis is responsible for nearly all deaths caused by osteosarcoma, the most common pediatric bone tumor. How malignant bone cells coerce the lung microenvironment to support metastatic growth is unclear. The purpose of this study is to identify metastasis-specific therapeutic vulnerabilities by delineating the cellular and molecular mechanisms underlying osteosarcoma lung metastatic niche formation. EXPERIMENTAL DESIGN: Using single-cell transcriptomics (scRNA-seq), we characterized genome- and tissue-wide molecular changes induced within lung tissues by disseminated osteosarcoma cells in both immunocompetent murine models of metastasis and patient samples. We confirmed transcriptomic findings at the protein level and determined spatial relationships with multi-parameter immunofluorescence and spatial transcriptomics. Based on these findings, we evaluated the ability of nintedanib, a kinase inhibitor used to treat patients with pulmonary fibrosis, to impair metastasis progression in both immunocompetent murine osteosarcoma and immunodeficient human xenograft models. Single-nucleus and spatial transcriptomics was used to perform molecular pharmacodynamic studies that define the effects of nintedanib on tumor and non-tumor cells within the metastatic microenvironment. RESULTS: Osteosarcoma cells induced acute alveolar epithelial injury upon lung dissemination. scRNA-seq demonstrated that the surrounding lung stroma adopts a chronic, non-resolving wound-healing phenotype similar to that seen in other models of lung injury. Accordingly, metastasis-associated lung demonstrated marked fibrosis, likely due to the accumulation of pathogenic, pro-fibrotic, partially differentiated epithelial intermediates and macrophages. Our data demonstrated that nintedanib prevented metastatic progression in multiple murine and human xenograft models by inhibiting osteosarcoma-induced fibrosis. CONCLUSIONS: Fibrosis represents a targetable vulnerability to block the progression of osteosarcoma lung metastasis. Our data support a model wherein interactions between osteosarcoma cells and epithelial cells create a pro-metastatic niche by inducing tumor deposition of extracellular matrix proteins such as fibronectin that is disrupted by the anti-fibrotic TKI nintedanib. Our data shed light on the non-cell autonomous effects of TKIs on metastasis and provide a roadmap for using single-cell and spatial transcriptomics to define the mechanism of action of TKI on metastases in animal models.

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem.

Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data.

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

Analyzing Spatial Transcriptomics Data Using Giotto.

Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user-friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Getting Giotto set up for use Basic Protocol 2: Pre-processing Basic Protocol 3: Clustering and cell-type identification Basic Protocol 4: Cell-type enrichment and deconvolution analyses Basic Protocol 5: Spatial structure analysis tools Basic Protocol 6: Spatial domain detection by using a hidden Markov random field model Support Protocol 1: Spatial proximity-associated cell-cell interactions Support Protocol 2: Assembly of a registered 3D Giotto object from 2D slices.

The crosstalk within the breast tumor microenvironment in type II diabetes: Implications for cancer disparities.

Obesity-driven (type 2) diabetes (T2D), the most common metabolic disorder, both increases the incidence of all molecular subtypes of breast cancer and decreases survival in postmenopausal women. Despite this clear link, T2D and the associated dysfunction of diverse tissues is often not considered during the standard of care practices in oncology and, moreover, is treated as exclusion criteria for many emerging clinical trials. These guidelines have caused the biological mechanisms that associate T2D and breast cancer to be understudied. Recently, it has been illustrated that the breast tumor microenvironment (TME) composition and architecture, specifically the surrounding cellular and extracellular structures, dictate tumor progression and are directly relevant for clinical outcomes. In addition to the epithelial cancer cell fraction, the breast TME is predominantly made up of cancer-associated fibroblasts, adipocytes, and is often infiltrated by immune cells. During T2D, signal transduction among these cell types is aberrant, resulting in a dysfunctional breast TME that communicates with nearby cancer cells to promote oncogenic processes, cancer stem-like cell formation, pro-metastatic behavior and increase the risk of recurrence. As these cells are non-malignant, despite their signaling abnormalities, data concerning their function is never captured in DNA mutational databases, thus we have limited insight into mechanism from publicly available datasets. We suggest that abnormal adipocyte and immune cell exhaustion within the breast TME in patients with obesity and metabolic disease may elicit greater transcriptional plasticity and cellular heterogeneity within the expanding population of malignant epithelial cells, compared to the breast TME of a non-obese, metabolically normal patient. These challenges are particularly relevant to cancer disparities settings where the fraction of patients seen within the breast medical oncology practice also present with co-morbid obesity and metabolic disease. Within this review, we characterize the changes to the breast TME during T2D and raise urgent molecular, cellular and translational questions that warrant further study, considering the growing prevalence of T2D worldwide.

Multi-modal profiling of human fetal liver hematopoietic stem cells reveals the molecular signature of engraftment.

The human hematopoietic stem cell harbors remarkable regenerative potential that can be harnessed therapeutically. During early development, hematopoietic stem cells in the fetal liver undergo active expansion while simultaneously retaining robust engraftment capacity, yet the underlying molecular program responsible for their efficient engraftment remains unclear. Here, we profile 26,407 fetal liver cells at both the transcriptional and protein level including ~7,000 highly enriched and functional fetal liver hematopoietic stem cells to establish a detailed molecular signature of engraftment potential. Integration of transcript and linked cell surface marker expression reveals a generalizable signature defining functional fetal liver hematopoietic stem cells and allows for the stratification of enrichment strategies with high translational potential. More precisely, our integrated analysis identifies CD201 (endothelial protein C receptor (EPCR), encoded by PROCR) as a marker that can specifically enrich for engraftment potential. This comprehensive, multi-modal profiling of engraftment capacity connects a critical biological function at a key developmental timepoint with its underlying molecular drivers. As such, it serves as a useful resource for the field and forms the basis for further biological exploration of strategies to retain the engraftment potential of hematopoietic stem cells ex vivo or induce this potential during in vitro hematopoietic stem cell generation.

Mesenchymal and adrenergic cell lineage states in neuroblastoma possess distinct immunogenic phenotypes.

Apart from the anti-GD2 antibody, immunotherapy for neuroblastoma has had limited success due to immune evasion mechanisms, coupled with an incomplete understanding of predictors of response. Here, from bulk and single-cell transcriptomic analyses, we identify a subset of neuroblastomas enriched for transcripts associated with immune activation and inhibition and show that these are predominantly characterized by gene expression signatures of the mesenchymal lineage state. By contrast, tumors expressing adrenergic lineage signatures are less immunogenic. The inherent presence or induction of the mesenchymal state through transcriptional reprogramming or therapy resistance is accompanied by innate and adaptive immune gene activation through epigenetic remodeling. Mesenchymal lineage cells promote T cell infiltration by secreting inflammatory cytokines, are efficiently targeted by cytotoxic T and natural killer cells and respond to immune checkpoint blockade. Together, we demonstrate that distinct immunogenic phenotypes define the divergent lineage states of neuroblastoma and highlight the immunogenic potential of the mesenchymal lineage.

Giotto: a toolbox for integrative analysis and visualization of spatial expression data.

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

Extracellular domain shedding of the ALK receptor mediates neuroblastoma cell migration.

Although activating mutations of the anaplastic lymphoma kinase (ALK) membrane receptor occur in ∼10% of neuroblastoma (NB) tumors, the role of the wild-type (WT) receptor, which is aberrantly expressed in most non-mutated cases, is unclear. Both WT and mutant proteins undergo extracellular domain (ECD) cleavage. Here, we map the cleavage site to Asn654-Leu655 and demonstrate that cleavage inhibition of WT ALK significantly impedes NB cell migration with subsequent prolongation of survival in mouse models. Cleavage inhibition results in the downregulation of an epithelial-to-mesenchymal transition (EMT) gene signature, with decreased nuclear localization and occupancy of ß-catenin at EMT gene promoters. We further show that cleavage is mediated by matrix metalloproteinase 9, whose genetic and pharmacologic inactivation inhibits cleavage and decreases NB cell migration. Together, our results indicate a pivotal role for WT ALK ECD cleavage in NB pathogenesis, which may be harnessed for therapeutic benefit.