Germinal Center Dark Zone harbors ATR-dependent determinants of T-cell exclusion that are also identified in aggressive lymphoma.

The germinal center (GC) dark zone (DZ) and light zone (LZ) regions spatially separate expansion and diversification from selection of antigen-specific B-cells to ensure antibody affinity maturation and B cell memory. The DZ and LZ differ significantly in their immune composition despite the lack of a physical barrier, yet the determinants of this polarization are poorly understood. This study provides novel insights into signals controlling asymmetric T-cell distribution between DZ and LZ regions. We identify spatially-resolved DNA damage response and chromatin compaction molecular features that underlie DZ T-cell exclusion. The DZ spatial transcriptional signature linked to T-cell immune evasion clustered aggressive Diffuse Large B-cell Lymphomas (DLBCL) for differential T cell infiltration. We reveal the dependence of the DZ transcriptional core signature on the ATR kinase and dissect its role in restraining inflammatory responses contributing to establishing an immune-repulsive imprint in DLBCL. These insights may guide ATR-focused treatment strategies bolstering immunotherapy in tumors marked by DZ transcriptional and chromatin-associated features.

Detecting radio- and chemoresistant cells in 3D cancer co-cultures using chromatin biomarkers.

The heterogenous treatment response of tumor cells limits the effectiveness of cancer therapy. While this heterogeneity has been linked to cell-to-cell variability within the complex tumor microenvironment, a quantitative biomarker that identifies and characterizes treatment-resistant cell populations is still missing. Herein, we use chromatin organization as a cost-efficient readout of the cells' states to identify subpopulations that exhibit distinct responses to radiotherapy. To this end, we developed a 3D co-culture model of cancer spheroids and patient-derived fibroblasts treated with radiotherapy. Using the model we identified treatment-resistant cells that bypassed DNA damage checkpoints and exhibited an aggressive growth phenotype. Importantly, these cells featured more condensed chromatin which primed them for treatment evasion, as inhibiting chromatin condensation and DNA damage repair mechanisms improved the efficacy of not only radio- but also chemotherapy. Collectively, our work shows the potential of using chromatin organization to cost-effectively study the heterogeneous treatment susceptibility of cells and guide therapeutic design.

Actomyosin contractility as a mechanical checkpoint for cell state transitions.

Cell state transitions induced by mechano-chemical cues result in a heterogeneous population of cell states. While much of the work towards understanding the origins of such heterogeneity has focused on the gene regulatory mechanisms, the contribution of intrinsic mechanical properties of cells remains unknown. In this paper, using a well-defined single cell platform to induce cell-state transitions, we reveal the importance of actomyosin contractile forces in regulating the heterogeneous cell-fate decisions. Temporal analysis of laterally confined growth of fibroblasts revealed sequential changes in the colony morphology which was tightly coupled to the progressive erasure of lineage-specific transcription programs. Pseudo-trajectory constructed using unsupervised diffusion analysis of the colony morphology features revealed a bifurcation event in which some cells undergo successful cell state transitions towards partial reprogramming. Importantly, inhibiting actomyosin contractility before the bifurcation event leads to more efficient dedifferentiation. Taken together, this study highlights the presence of mechanical checkpoints that contribute to the heterogeneity in cell state transitions.

Single cell imaging-based chromatin biomarkers for tumor progression.

Tumour progression within the tissue microenvironment is accompanied by complex biomechanical alterations of the extracellular environment. While histopathology images provide robust biochemical markers for tumor progression in clinical settings, a quantitative single cell score using nuclear morphology and chromatin organization integrated with the long range mechanical coupling within the tumor microenvironment is missing. We propose that the spatial chromatin organization in individual nuclei characterises the cell state and their alterations during tumor progression. In this paper, we first built an image analysis pipeline and implemented it to classify nuclei from patient derived breast tissue biopsies of various cancer stages based on their nuclear and chromatin features. Replacing H&E with DNA binding dyes such as Hoescht stained tissue biopsies, we improved the classification accuracy. Using the nuclear morphology and chromatin organization features, we constructed a pseudo-time model to identify the chromatin state changes that occur during tumour progression. This enabled us to build a single-cell mechano-genomic score that characterises the cell state during tumor progression from a normal to a metastatic state. To gain further insights into the alterations in the local tissue microenvironments, we also used the nuclear orientations to identify spatial neighbourhoods that have been posited to drive tumor progression. Collectively, we demonstrate that image-based single cell chromatin and nuclear features are important single cell biomarkers for phenotypic mapping of tumor progression.

Multi-domain translation between single-cell imaging and sequencing data using autoencoders.

The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

Predicting cell lineages using autoencoders and optimal transport.

Lineage tracing involves the identification of all ancestors and descendants of a given cell, and is an important tool for studying biological processes such as development and disease progression. However, in many settings, controlled time-course experiments are not feasible, for example when working with tissue samples from patients. Here we present ImageAEOT, a computational pipeline based on autoencoders and optimal transport for predicting the lineages of cells using time-labeled datasets from different stages of a cellular process. Given a single-cell image from one of the stages, ImageAEOT generates an artificial lineage of this cell based on the population characteristics of the other stages. These lineages can be used to connect subpopulations of cells through the different stages and identify image-based features and biomarkers underlying the biological process. To validate our method, we apply ImageAEOT to a benchmark task based on nuclear and chromatin images during the activation of fibroblasts by tumor cells in engineered 3D tissues. We further validate ImageAEOT on chromatin images of various breast cancer cell lines and human tissue samples, thereby linking alterations in chromatin condensation patterns to different stages of tumor progression. Our results demonstrate the promise of computational methods based on autoencoding and optimal transport principles for lineage tracing in settings where existing experimental strategies cannot be used.

Multivariate analysis reveals activation-primed fibroblast geometric states in engineered 3D tumor microenvironments.

Fibroblasts are a heterogeneous group of cells comprising subpopulations that have been found to be activated in the stromal microenvironment that regulates tumor initiation and growth. The underlying mechanisms of such selective activation of fibroblasts are not understood. We propose that the intrinsic geometric heterogeneity of fibroblasts modulates the nuclear mechanotransduction of signals from the microenvironment, resulting in their selective activation. To test this, we developed an engineered 3D fibroblast tumor coculture system and used high resolution images to quantify multiple cell geometry sensitive nuclear morphological and chromatin organizational features. These features were then mapped to activation levels as measured by the nuclear abundance of transcription cofactor, megakaryoblastic leukemia, and protein levels of its target, αSMA. Importantly, our results indicate the presence of activation-"primed" cell geometries that present higher activation levels, which are further enhanced in the presence of stimuli from cancer cells. Further, we show that by enriching the population of activation-primed cell geometric states by either increasing matrix rigidity or micropatterning primed cell shapes, fibroblast activation levels can be increased. Collectively, our results reveal important cellular geometric states that select for fibroblast activation within the heterogenous tumor microenvironment.

Compressive force induces reversible chromatin condensation and cell geometry-dependent transcriptional response.

Fibroblasts exhibit heterogeneous cell geometries in tissues and integrate both mechanical and biochemical signals in their local microenvironment to regulate genomic programs via chromatin remodelling. While in connective tissues fibroblasts experience tensile and compressive forces (CFs), the role of compressive forces in regulating cell behavior and, in particular, the impact of cell geometry in modulating transcriptional response to such extrinsic mechanical forces is unclear. Here we show that CF on geometrically well-defined mouse fibroblast cells reduces actomyosin contractility and shuttles histone deacetylase 3 (HDAC3) into the nucleus. HDAC3 then triggers an increase in the heterochromatin content by initiating removal of acetylation marks on the histone tails. This suggests that, in response to CF, fibroblasts condense their chromatin and enter into a transcriptionally less active and quiescent states as also revealed by transcriptome analysis. On removal of CF, the alteration in chromatin condensation was reversed. We also present a quantitative model linking CF-dependent changes in actomyosin contractility leading to chromatin condensation. Further, transcriptome analysis also revealed that the transcriptional response of cells to CF was geometry dependent. Collectively, our results suggest that CFs induce chromatin condensation and geometry-dependent differential transcriptional response in fibroblasts that allows maintenance of tissue homeostasis.

Laterally confined growth of cells induces nuclear reprogramming in the absence of exogenous biochemical factors.

Cells in tissues undergo transdifferentiation programs when stimulated by specific mechanical and biochemical signals. While seminal studies have demonstrated that exogenous biochemical factors can reprogram somatic cells into pluripotent stem cells, the critical roles played by mechanical signals in such reprogramming process have not been well documented. In this paper, we show that laterally confined growth of fibroblasts on micropatterned substrates induces nuclear reprogramming with high efficiency in the absence of any exogenous reprogramming factors. We provide compelling evidence on the induction of stem cell-like properties using alkaline phosphatase assays and expression of pluripotent markers. Early onset of reprogramming was accompanied with enhanced nuclear dynamics and changes in chromosome intermingling degrees, potentially facilitating rewiring of the genome. Time-lapse analysis of promoter occupancy by immunoprecipitation of H3K9Ac chromatin fragments revealed that epithelial, proliferative, and reprogramming gene promoters were progressively acetylated, while mesenchymal promoters were deacetylated by 10 days. Consistently, RNA sequencing analysis showed a systematic progression from mesenchymal to stem cell transcriptome, highlighting pathways involving mechanisms underlying nuclear reprogramming. We then demonstrated that these mechanically reprogrammed cells could be maintained as stem cells and can be redifferentiated into multiple lineages with high efficiency. Importantly, we also demonstrate the induction of cancer stemness properties in MCF7 cells grown in such laterally confined conditions. Collectively, our results highlight an important generic property of somatic cells that, when grown in laterally confined conditions, acquire stemness. Such mechanical reprogramming of somatic cells demonstrated here has important implications in tissue regeneration and disease models.

Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription.

The 3D structure of the genome plays a key role in regulatory control of the cell. Experimental methods such as high-throughput chromosome conformation capture (Hi-C) have been developed to probe the 3D structure of the genome. However, it remains a challenge to deduce from these data chromosome regions that are colocalized and coregulated. Here, we present an integrative approach that leverages 1D functional genomic features (e.g., epigenetic marks) with 3D interactions from Hi-C data to identify functional interchromosomal interactions. We construct a weighted network with 250-kb genomic regions as nodes and Hi-C interactions as edges, where the edge weights are given by the correlation between 1D genomic features. Individual interacting clusters are determined using weighted correlation clustering on the network. We show that intermingling regions generally fall into either active or inactive clusters based on the enrichment for RNA polymerase II (RNAPII) and H3K9me3, respectively. We show that active clusters are hotspots for transcription factor binding sites. We also validate our predictions experimentally by 3D fluorescence in situ hybridization (FISH) experiments and show that active RNAPII is enriched in predicted active clusters. Our method provides a general quantitative framework that couples 1D genomic features with 3D interactions from Hi-C to probe the guiding principles that link the spatial organization of the genome with regulatory control.

Cell geometry dictates TNFα-induced genome response.

Cells in physiology integrate local soluble and mechanical signals to regulate genomic programs. Whereas the individual roles of these signals are well studied, the cellular responses to the combined chemical and physical signals are less explored. Here, we investigated the cross-talk between cellular geometry and TNFα signaling. We stabilized NIH 3T3 fibroblasts into rectangular anisotropic or circular isotropic geometries and stimulated them with TNFα and analyzed nuclear translocation of transcription regulators -NFκB (p65) and MKL and downstream gene-expression patterns. We found that TNFα induces geometry-dependent actin depolymerization, which enhances IκB degradation, p65 nuclear translocation, nuclear exit of MKL, and sequestration of p65 at the RNA-polymerase-II foci. Further, global transcription profile of cells under matrix-TNFα interplay reveals a geometry-dependent gene-expression pattern. At a functional level, we find cell geometry affects TNFα-induced cell proliferation. Our results provide compelling evidence that fibroblasts, depending on their geometries, elicit distinct cellular responses for the same cytokine.

Nuclear Positioning and Its Translational Dynamics Are Regulated by Cell Geometry.

The collective activity of several molecular motors and other active processes generate large forces for directional motion within the cell, which is vital for a multitude of cellular functions such as migration, division, contraction, transport, and positioning of various organelles. These processes also generate a background of fluctuating forces, which influence intracellular dynamics and thereby create unique biophysical signatures, which are altered in many diseases. In this study, we have used the nucleus as a probe particle to understand the microrheological properties of altered intracellular environments by using micropatterning to confine cells in two structurally and functionally extreme geometries. We find that nuclear positional dynamics is sensitive to the cytoskeletal organization by studying the effect of actin polymerization and nuclear rigidity on the diffusive behavior of the nucleus. Taken together, our results suggest that mapping nuclear positional dynamics provides important insights into biophysical properties of the active cytoplasmic medium. These biophysical signatures have the potential to be used as an ultrasensitive single-cell assay for early disease diagnostics.