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Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. While rare cell subpopulations frequently make the difference between health and disease, their detection remains a challenge. Here, the authors devise CellCnn, a representation learning approach to detecting such rare cell populations from high-dimensional single cell data, and, among other examples, demonstrate its capacity for detecting rare leukaemic blasts in minimal residual disease.

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

Chronic viral infections are often associated with impaired CD8 + T cell function, referred to as exhaustion. Although the molecular and cellular circuits involved in CD8 + T cell exhaustion are well defined, with sustained presence of antigen being one important parameter, how much T cell receptor (TCR) signaling is actually ongoing in vivo during established chronic infection is unclear. Here, we characterize the in vivo TCR signaling of virus-specific exhausted CD8 + T cells in a mouse model, leveraging TCR signaling reporter mice in combination with transcriptomics. In vivo signaling in exhausted cells is low, in contrast to their in vitro signaling potential, and despite antigen being abundantly present. Both checkpoint blockade and adoptive transfer of naïve target cells increase TCR signaling, demonstrating that engagement of co-inhibitory receptors curtails CD8 + T cell signaling and function in vivo. Excess antigenic exposure, such as in cancers or chronic viral infection, can lead to T cell exhaustion. Here the authors show that despite high exposure to antigen in the context of chronic LCMV infection in mice, exhausted CD8 + T cells have low levels of TCR signalling that can be reactivated by PD-L1 blockade.

Temperature-induced cell death is thought to be due to protein denaturation, but the determinants of thermal sensitivity of proteomes remain largely uncharacterized. We developed a structural proteomic strategy to measure protein thermostability on a proteome-wide scale and with domain-level resolution. We applied it to , , , and human cells, yielding thermostability data for more than 8000 proteins. Our results (i) indicate that temperature-induced cellular collapse is due to the loss of a subset of proteins with key functions, (ii) shed light on the evolutionary conservation of protein and domain stability, and (iii) suggest that natively disordered proteins in a cell are less prevalent than predicted and (iv) that highly expressed proteins are stable because they are designed to tolerate translational errors that would lead to the accumulation of toxic misfolded species.

Trajectory inference refers to the task of reconstructing state sequences of dynamic processes from single-cell RNA sequencing (scRNAseq) data. This task frequently results in ambiguous results due to the noisiness of the data. While this issue has been alleviated by the incorporation of directional information from RNA velocity analyses, it remains difficult to resolve complex differentiation topologies, such as convergent trajectories. We introduce exploratory trajectory inference to address this challenge. This approach considers unsupervised clustering analysis of trajectory ensembles derived from simulation-based trajectory inference to deduce differentiation lineages in a data-driven fashion. We assess this approach to resolve the convergent differentiation trajectories in CD8 T-cell differentiation in chronic infections. We utilize an original scRNAseq time-series dataset of CD8 T cells collected during the time course of a chronic LCMV infection. Simulation-based trajectory inference identified a branch region early during chronic infection where cells separate into an exhausted and a memory-like lineage. Exploratory trajectory inference further allowed us to identify a convergent differentiation trajectory traversing memory-like states and ending in the exhausted population. Adoptive transfer experiments showed CD8 T cells with predicted memory-like fate differentiating into both memory-like and exhaustion states, confirming the convergent differentiation topology. We expect exploratory trajectory inference to be applicable in other scRNAseq-based studies aiming at comprehensive characterization of differentiation trajectories with bifurcating and convergent topologies.

Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.

Single-cell RNA sequencing (scRNA-seq) approaches have transformed our ability to resolve cellular properties across systems, but are currently tailored toward large cell inputs (>1,000 cells). This renders them inefficient and costly when processing small, individual tissue samples, a problem that tends to be resolved by loading bulk samples, yielding confounded mosaic cell population read-outs. Here, we developed a deterministic, mRNA-capture bead and cell co-encapsulation dropleting system, DisCo, aimed at processing low-input samples (<500 cells). We demonstrate that DisCo enables precise particle and cell positioning and droplet sorting control through combined machine-vision and multilayer microfluidics, enabling continuous processing of low-input single-cell suspensions at high capture efficiency (>70%) and at speeds up to 350 cells per hour. To underscore DisCo’s unique capabilities, we analyzed 31 individual intestinal organoids at varying developmental stages. This revealed extensive organoid heterogeneity, identifying distinct subtypes including a regenerative fetal-like Ly6a + stem cell population that persists as symmetrical cysts, or spheroids, even under differentiation conditions, and an uncharacterized ‘gobloid’ subtype consisting predominantly of precursor and mature ( Muc 2 + ) goblet cells. To complement this dataset and to demonstrate DisCo’s capacity to process low-input, in vivo-derived tissues, we also analyzed individual mouse intestinal crypts. This revealed the existence of crypts with a compositional similarity to spheroids, which consisted predominantly of regenerative stem cells, suggesting the existence of regenerating crypts in the homeostatic intestine. These findings demonstrate the unique power of DisCo in providing high-resolution snapshots of cellular heterogeneity in small, individual tissues. DisCo is a deterministic droplet microfluidics tool for single-cell analysis on low cell input samples, which is demonstrated to profile individual intestinal organoids and in vivo-derived small tissues.

The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit goals of systems biology that require the knowledge of the absolute quantity of the system's components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ∼10 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution. The majority of all proteins expressed in the human osteosarcoma cell line U2OS were absolutely quantified by mass spectrometry. The quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell.

Highly multiplexed single-cell imaging technologies have revolutionized our ability to capture spatial protein expression at the single-cell level, thereby enabling a deeper understanding of tissue organization and function. However, these advancements rely on accurate cell segmentation, which defines cell boundaries to generate expression profiles. Despite its importance, there is a gap in quantifying how segmentation inaccuracies propagate through analytical pipelines, particularly affecting cell clustering and phenotyping. We introduce a framework that uses affine transformations to simulate realistic segmentation errors. Our approach mimics the variations induced by segmentation algorithms, allowing us to evaluate the robustness of downstream analyses under controlled perturbation conditions. We show that even moderate segmentation errors can significantly distort estimated protein profiles and disrupt cellular neighborhood relationships in feature space. Effects are most pronounced in clustering analyses, where both unsupervised k-Means and graph-based Leiden algorithms exhibit reduced consistency with increasing perturbation — especially with smaller neighborhood sizes. Similarly, cell phenotyping via Gaussian Mixture Models is adversely impacted, with higher levels of segmentation error leading to notable misclassifications between closely related cell types. These results highlight the importance of ensuring high-quality segmentation and careful data processing strategies to mitigate spurious results for downstream analysis tasks. Considering segmentation inaccuracies, possibly in a probabilistic modeling framework, will improve the reliability and reproducibility of findings in multiplexed tissue imaging studies.

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.