Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS.

Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.

Chromatin Remodeling Due to Transient-Link-and-Pass Activity Enhances Subnuclear Dynamics.

Spatiotemporal coordination of chromatin and subnuclear compartments is crucial for cells. Numerous enzymes act inside nucleus-some of those transiently link and pass two chromatin segments. Here, we study how such an active perturbation affects fluctuating dynamics of an inclusion in the chromatic medium. Using numerical simulations and a versatile effective model, we categorize inclusion dynamics into three distinct modes. The transient-link-and-pass activity speeds up inclusion dynamics by affecting a slow mode related to chromatin remodeling, viz., size and shape of the chromatin meshes.

Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging.

Human life expectancy is constantly increasing and aging has become a major risk factor for many diseases, although the underlying gene regulatory mechanisms are still unclear. Using transcriptomic and chromosomal conformation capture (Hi-C) data from human skin fibroblasts from individuals across different age groups, we identified a tight coupling between the changes in co-regulation and co-localization of genes. We obtained transcription factors, cofactors, and chromatin regulators that could drive the cellular aging process by developing a time-course prize-collecting Steiner tree algorithm. In particular, by combining RNA-Seq data from different age groups and protein-protein interaction data we determined the key transcription regulators and gene regulatory changes at different life stage transitions. We then mapped these transcription regulators to the 3D reorganization of chromatin in young and old skin fibroblasts. Collectively, we identified key transcription regulators whose target genes are spatially rearranged and correlate with changes in their expression, thereby providing potential targets for reverting cellular aging.

Implanting mechanically reprogrammed fibroblasts for aged tissue regeneration and wound healing.

Cell-based therapies are essential for tissue regeneration and wound healing during aging. Autologous transplantation of aging cells is ineffective due to their increased senescence and reduced tissue remodeling capabilities. Alternatively, implanting reprogrammed aged cells provides unique opportunities. In this paper, we demonstrate the implantation of partially reprogrammed aged human dermal fibroblasts into in vitro aged skin models for tissue regeneration and wound healing. The partially reprogrammed cells were obtained using our previously reported, highly efficient mechanical approach. Implanted cells showed enhanced expression of extracellular matrix proteins in the large area of aged tissue. In addition, the implanted cells at wound sites showed increased extracellular matrix protein synthesis and matrix alignment. Transcriptome analysis, combined with chromatin biomarkers, revealed these implanted cells upregulated tissue regeneration and wound healing pathways. Collectively our results provide a novel, nongenetic, partial reprogramming of aged cells for cell-based therapies in regenerative medicine.

Imaging and AI based chromatin biomarkers for diagnosis and therapy evaluation from liquid biopsies.

Multiple genomic and proteomic studies have suggested that peripheral blood mononuclear cells (PBMCs) respond to tumor secretomes and thus could provide possible avenues for tumor prognosis and treatment evaluation. We hypothesized that the chromatin organization of PBMCs obtained from liquid biopsies, which integrates secretome signals with gene expression programs, provides efficient biomarkers to characterize tumor signals and the efficacy of proton therapy in tumor patients. Here, we show that chromatin imaging of PBMCs combined with machine learning methods provides such robust and predictive chromatin biomarkers. We show that such chromatin biomarkers enable the classification of 10 healthy and 10 pan-tumor patients. Furthermore, we extended our pipeline to assess the tumor types and states of 30 tumor patients undergoing (proton) radiation therapy. We show that our pipeline can thereby accurately distinguish between three tumor groups with up to 89% accuracy and enables the monitoring of the treatment effects. Collectively, we show the potential of chromatin biomarkers for cancer diagnostics and therapy evaluation.

Adhesome Receptor Clustering is Accompanied by the Colocalization of the Associated Genes in the Cell Nucleus.

Proteins on the cell membrane cluster to respond to extracellular signals; for example, adhesion proteins cluster to enhance extracellular matrix sensing; or T-cell receptors cluster to enhance antigen sensing. Importantly, the maturation of such receptor clusters requires transcriptional control to adapt and reinforce the extracellular signal sensing. However, it has been unclear how such efficient clustering mechanisms are encoded at the level of the genes that code for these receptor proteins. Using the adhesome as an example, we show that genes that code for adhesome receptor proteins are spatially co-localized and co-regulated within the cell nucleus. Towards this, we use Hi-C maps combined with RNA-seq data of adherent cells to map the correspondence between adhesome receptor proteins and their associated genes. Interestingly, we find that the transcription factors that regulate these genes are also co-localized with the adhesome gene loci, thereby potentially facilitating a transcriptional reinforcement of the extracellular matrix sensing machinery. Collectively, our results highlight an important layer of transcriptional control of cellular signal sensing.

Mechanical forces and the 3D genome.

Traditionally, the field of genomics has been studied from a biochemical perspective. Besides chemical influences, cells are subject to a variety of mechanical signals from their surrounding tissue microenvironment. These mechanical signals can not only cause changes to a cell's physical structure but can also lead to alterations in their genomes and gene expression programs. Understanding the mechanical control of genome organization and expression may provide a new perspective on gene regulation.

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.

Deleterious Mechanical Deformation Selects Mechanoresilient Cancer Cells with Enhanced Proliferation and Chemoresistance.

Cancer cells in secondary tumors are found to form metastases more efficiently as compared to their primary tumor counterparts. This is partially due to the unfavorable microenvironments encountered by metastasizing cancer cells that result in the survival of a more metastatic phenotype from the original population. However, the role of deleterious mechanical stresses in this change of metastatic potential is unclear. Here, by forcing cancer cells to flow through small capillary-sized constrictions, it is demonstrated that mechanical deformation can select a tumor cell subpopulation that exhibits resilience to mechanical squeezing-induced cell death. Transcriptomic profiling reveals up-regulated proliferation and DNA damage response pathways in this subpopulation, which are further translated into a more proliferative and chemotherapy-resistant phenotype. These results highlight a potential link between the microenvironmental physical stresses and the enhanced malignancy of metastasizing cancer cells which may be utilized as a therapeutic strategy in preventing the metastatic spread of cancer cells.

How enzymatic activity is involved in chromatin organization.

Spatial organization of chromatin plays a critical role in genome regulation. Previously, various types of affinity mediators and enzymes have been attributed to regulate spatial organization of chromatin from a thermodynamics perspective. However, at the mechanistic level, enzymes act in their unique ways and perturb the chromatin. Here, we construct a polymer physics model following the mechanistic scheme of Topoisomerase-II, an enzyme resolving topological constraints of chromatin, and investigate how it affects interphase chromatin organization. Our computer simulations demonstrate Topoisomerase-II's ability to phase separate chromatin into eu- and heterochromatic regions with a characteristic wall-like organization of the euchromatic regions. We realized that the ability of the euchromatic regions to cross each other due to enzymatic activity of Topoisomerase-II induces this phase separation. This realization is based on the physical fact that partial absence of self-avoiding interaction can induce phase separation of a system into its self-avoiding and non-self-avoiding parts, which we reveal using a mean-field argument. Furthermore, motivated from recent experimental observations, we extend our model to a bidisperse setting and show that the characteristic features of the enzymatic activity-driven phase separation survive there. The existence of these robust characteristic features, even under the non-localized action of the enzyme, highlights the critical role of enzymatic activity in chromatin organization.

Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer's disease.

Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI incorporates multiple modalities in a single representation for downstream tasks, enables the prediction of spatial transcriptomic data from nuclear images in unseen tissue sections, and provides built-in batch correction of gene expression and tissue morphology through over-parameterization. We apply STACI to analyze the spatio-temporal progression of Alzheimer's disease and identify the associated nuclear morphometric and coupled gene expression features. Collectively, we demonstrate the importance of characterizing disease progression by integrating multiple data modalities and its potential for the discovery of disease biomarkers.

Lateral confined growth of cells activates Lef1 dependent pathways to regulate cell-state transitions.

Long-term sustained mechano-chemical signals in tissue microenvironment regulate cell-state transitions. In recent work, we showed that laterally confined growth of fibroblasts induce dedifferentiation programs. However, the molecular mechanisms underlying such mechanically induced cell-state transitions are poorly understood. In this paper, we identify Lef1 as a critical somatic transcription factor for the mechanical regulation of de-differentiation pathways. Network optimization methods applied to time-lapse RNA-seq data identify Lef1 dependent signaling as potential regulators of such cell-state transitions. We show that Lef1 knockdown results in the down-regulation of fibroblast de-differentiation and that Lef1 directly interacts with the promoter regions of downstream reprogramming factors. We also evaluate the potential upstream activation pathways of Lef1, including the Smad4, Atf2, NFkB and Beta-catenin pathways, thereby identifying that Smad4 and Atf2 may be critical for Lef1 activation. Collectively, we describe an important mechanotransduction pathway, including Lef1, which upon activation, through progressive lateral cell confinement, results in fibroblast de-differentiation.

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.

Guiding Irregular Nuclear Morphology on Nanopillar Arrays for Malignancy Differentiation in Tumor Cells.

For more than a century, abnormal nuclei in tumor cells, presenting subnuclear invaginations and folds on the nuclear envelope, have been known to be associated with high malignancy and poor prognosis. However, current nuclear morphology analysis focuses on the features of the entire nucleus, overlooking the malignancy-related subnuclear features in nanometer scale. The main technical challenge is to probe such tiny and randomly distributed features inside cells. We here employ nanopillar arrays to guide subnuclear features into ordered patterns, enabling their quantification as a strong indicator of cell malignancy. Both breast and liver cancer cells were validated as well as the quantification of nuclear abnormality heterogeneity. The alterations of subnuclear patterns were also explored as effective readouts for drug treatment. We envision that this nanopillar-enabled quantification of subnuclear abnormal features in tumor cells opens a new angle in characterizing malignant cells and studying the unique nuclear biology in cancer.

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.

The characteristics of nuclear membrane fluctuations in stem cells.

We analyse the stem cell nucleus shape fluctuation spectrum obtained from optical confocal microscopy on an hour time scale with 10 s resolution. In particular, we investigate the angular and time dependencies of these fluctuations, define appropriate correlation functions that reveal the fundamentally out of equilibrium nature of the observed fluctuations as well as their global anisotropy. Langevin equations respecting the symmetry of the system allow us to model the damped oscillatory behaviour of the time correlations.

Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity.

In this Current Opinion, we highlight the importance of the material properties of tissues and how alterations therein, which influence epithelial-to-mesenchymal transitions, represent an important layer of regulation in a number of diseases and potentially also play a critical role in host-pathogen interactions. In light of the current SARS-CoV-2 pandemic, we here highlight the possible role of lung tissue stiffening with ageing and how this might facilitate increased SARS-CoV-2 replication through matrix-stiffness dependent epithelial-to-mesenchymal transitions of the lung epithelium. This emphasizes the need for integrating material properties of tissues in drug discovery programs.

Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing.

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

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.

Analysis of transcriptional modules during human fibroblast ageing.

For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts. By relating the modules to the donor's traits, we uncovered the natural aging- and premature aging disease-associated modules. The STRING functional association networks built with the core module memberships provided a systematic overview of genome-wide transcriptional changes upon aging. We validated the selected candidates via quantitative reverse transcription PCR (RT-qPCR) assay with young and aged human fibroblasts, and uncovered several genes involved in ECM, cell, and nuclear mechanics as a potential aging biomarker. Collectively, our study not only provides a snapshot of functional changes during human fibroblast aging but also presents potential aging markers that are relevant to cell mechanics.