Open-ST: High-resolution spatial transcriptomics in 3D.

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis.

In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.

Cellular senescence promotes progenitor cell expansion during axolotl limb regeneration.

Axolotl limb regeneration is accompanied by the transient induction of cellular senescence within the blastema, the structure that nucleates regeneration. The precise role of this blastemal senescent cell (bSC) population, however, remains unknown. Here, through a combination of gain- and loss-of-function assays, we elucidate the functions and molecular features of cellular senescence in vivo. We demonstrate that cellular senescence plays a positive role during axolotl regeneration by creating a pro-proliferative niche that supports progenitor cell expansion and blastema outgrowth. Senescent cells impact their microenvironment via Wnt pathway modulation. Further, we identify a link between Wnt signaling and senescence induction and propose that bSC-derived Wnt signals facilitate the proliferation of neighboring cells in part by preventing their induction into senescence. This work defines the roles of cellular senescence in the regeneration of complex structures.

The Rabl chromosome configuration masks a kinetochore reassembly mechanism in yeast mitosis.

During cell cycle progression in metazoans, the kinetochore is assembled at mitotic onset and disassembled during mitotic exit. Once assembled, the kinetochore complex attached to centromeres interacts directly with the spindle microtubules, the vehicle of chromosome segregation. This reassembly program is assumed to be absent in budding and fission yeast, because most kinetochore proteins are stably maintained at the centromeres throughout the entire cell cycle. Here, we show that the reassembly program of the outer kinetochore at mitotic onset is unexpectedly conserved in the fission yeast Schizosaccharomyces pombe. We identified this behavior by removing the Rabl chromosome configuration, in which centromeres are permanently associated with the nuclear envelope beneath the spindle pole body during interphase. In addition to having evolutionary implications for kinetochore reassembly, our results aid the understanding of the molecular processes responsible for kinetochore disassembly and assembly during mitotic entry.

ChroMo, an Application for Unsupervised Analysis of Chromosome Movements in Meiosis.

Nuclear movements during meiotic prophase, driven by cytoskeleton forces, are a broadly conserved mechanism in opisthokonts and plants to promote pairing between homologous chromosomes. These forces are transmitted to the chromosomes by specific associations between telomeres and the nuclear envelope during meiotic prophase. Defective chromosome movements (CMs) harm pairing and recombination dynamics between homologues, thereby affecting faithful gametogenesis. For this reason, modelling the behaviour of CMs and their possible microvariations as a result of mutations or physico-chemical stress is important to understand this crucial stage of meiosis. Current developments in high-throughput imaging and image processing are yielding large CM datasets that are suitable for data mining approaches. To facilitate adoption of data mining pipelines, we present ChroMo, an interactive, unsupervised cloud application specifically designed for exploring CM datasets from live imaging. ChroMo contains a wide selection of algorithms and visualizations for time-series segmentation, motif discovery, and assessment of causality networks. Using ChroMo to analyse meiotic CMs in fission yeast, we found previously undiscovered features of CMs and causality relationships between chromosome morphology and trajectory. ChroMo will be a useful tool for understanding the behaviour of meiotic CMs in yeast and other model organisms.