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Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data. Squidpy enables comprehensive analysis and visualization of spatial omics data and image with high efficiency.

Many wild animals perform hiding behaviours for a variety of reasons, such as evading predators or other conspecifics. Unlike their wild counterparts, farmed animals often live in relatively barren environments without the opportunity to hide. Researchers have begun to study the impact of access to hiding spaces (“hides”) in farmed animals, including possible effects on animal welfare. The aims of this scoping review were to: 1) identify the farmed species that have been most used in research investigating the provision of hides, 2) describe the context in which hides have been provided to farmed animals, and 3) describe the impact (positive, negative or neutral/inconclusive) that hides have on animals, including indicators of animal welfare. Three online databases (CAB Abstracts, Web of Science, and PubMed) were used to search for a target population of farmed animals with access to hiding spaces. From this search, 4,631 citations were screened and 151 were included in the review. Fourteen animal types were represented, most commonly chickens (48% of papers), cattle (9%), foxes (8%), and fish (7%). Relatively few papers were found on other species including deer, quail, ducks, lobsters, turkeys, and goats. Hides were used in four contexts: at parturition or oviposition (56%), for general enrichment (43%), for neonatal animals (4%), or for sick or injured animals (1%). A total of 218 outcomes relevant to our objectives were found including 7 categories: hide use, motivation, and/or preference (47% of outcomes), behavioural indicators of affective state (17%), health, injuries, and/or production (16%), agonistic behaviour (8%), abnormal repetitive behaviours (6%), physiological indicators of stress (5%), and affiliative behaviours (1%). Hiding places resulted in 162 positive (74%), 14 negative (6%), and 42 neutral/inconclusive (19%) outcomes. Hides had a generally positive impact on the animals included in this review; more research is encouraged for under-represented species.

Histological atlases of the cerebral cortex, such as those made famous by Brodmann and von Economo, are invaluable for understanding human brain microstructure and its relationship with functional organization in the brain. However, these existing atlases are limited to small numbers of manually annotated samples from a single cerebral hemisphere, measured from 2D histological sections. We present the first whole-brain quantitative 3D laminar atlas of the human cerebral cortex. It was derived from a 3D histological atlas of the human brain at 20-micrometer isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically, the cortical layers in both hemispheres. Our approach overcomes many of the historical challenges with measurement of histological thickness in 2D, and the resultant laminar atlas provides an unprecedented level of precision and detail. We utilized this BigBrain cortical atlas to test whether previously reported thickness gradients, as measured by MRI in sensory and motor processing cortices, were present in a histological atlas of cortical thickness and which cortical layers were contributing to these gradients. Cortical thickness increased across sensory processing hierarchies, primarily driven by layers III, V, and VI. In contrast, motor-frontal cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness from motor to frontal association cortices. These findings illustrate how this laminar atlas will provide a link between single-neuron morphology, mesoscale cortical layering, macroscopic cortical thickness, and, ultimately, functional neuroanatomy.

•Deep Learning facilitates high-throughput cytoarchitectonic mapping in large series of histological brain sections.•CNN-based analysis results in high-resolution 3D maps of cytoarchitectonic areas in the BigBrain model.•A new web-based workflow enables to train and execute neural networks interactively on a compute cluster. Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.

Human pancreas development remains incompletely characterized due to restricted sample access. We investigate whether pigs resemble humans in pancreas development, offering a complementary large-animal model. As pig pancreas organogenesis is unexplored, we first annotate developmental hallmarks throughout its 114-day gestation. Building on this, we construct a pig single-cell multiome pancreas atlas across all trimesters. Cross-species comparisons reveal pigs resemble humans more closely than mice in developmental tempo, epigenetic and transcriptional regulation, and gene regulatory networks. This further extends to progenitor dynamics and endocrine fate acquisition. Transcription factors regulated by NEUROG3, the endocrine master regulator, are over 50% conserved between pig and human, many being validated in human stem cell models. Notably, we uncover that during embryonic development, emerging beta-cell heterogeneity coincides with a species-conserved primed endocrine cell (PEC) population alongside NEUROG3-expressing cells. Overall, our work lays the foundation for comparative investigations and offers unprecedented insights into evolutionarily conserved pancreas organogenesis mechanisms across animal models. This study establishes the pig as a complementary model for studying human pancreas development. It shows pigs mimic human developmental tempo, gene regulation, and endocrine cell emergence, offering a valuable large-animal model for developmental biology.

The distribution of neurons in the cortex ( cytoarchitecture ) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations of currently used cytoarchitectonic mapping methods, but typically lack insight as to what extent they follow cytoarchitectonic principles. We therefore investigated in how far the internal structure of deep convolutional neural networks trained for cytoarchitectonic brain mapping reflect traditional cytoarchitectonic features, and compared them to features of the current grey level index (GLI) profile approach. The networks consisted of a 10-block deep convolutional architecture trained to segment the primary and secondary visual cortex. Filter activations of the networks served to analyse resemblances to traditional cytoarchitectonic features and comparisons to the GLI profile approach. Our analysis revealed resemblances to cellular, laminar- as well as cortical area related cytoarchitectonic features. The networks learned filter activations that reflect the distinct cytoarchitecture of the segmented cortical areas with special regard to their laminar organization and compared well to statistical criteria of the GLI profile approach. These results confirm an incorporation of relevant cytoarchitectonic features in the deep convolutional neural networks and mark them as a valid support for high-throughput cytoarchitectonic mapping workflows.

Diseases change over time, both phenotypically and in their underlying molecular processes. Though understanding disease progression dynamics is critical for diagnostics and treatment, capturing these dynamics is difficult due to their complexity and the high heterogeneity in disease development between individuals. We present TimeAx, an algorithm which builds a comparative framework for capturing disease dynamics using high-dimensional, short time-series data. We demonstrate the utility of TimeAx by studying disease progression dynamics for multiple diseases and data types. Notably, for urothelial bladder cancer tumorigenesis, we identify a stromal pro-invasion point on the disease progression axis, characterized by massive immune cell infiltration to the tumor microenvironment and increased mortality. Moreover, the continuous TimeAx model differentiates between early and late tumors within the same tumor subtype, uncovering molecular transitions and potential targetable pathways. Overall, we present a powerful approach for studying disease progression dynamics—providing improved molecular interpretability and clinical benefits for patient stratification and outcome prediction. Understanding disease progression dynamics is critical for diagnostics and treatment, but capturing dynamics is difficult. Here, the authors present a method for modelling disease progression from high dimensional molecular data that enables patient stratification and high-risk disease state identification, showcased in bladder cancer.

Many wild animals perform hiding behaviours for a variety of reasons, such as evading predators or other conspecifics. Unlike their wild counterparts, farmed animals often live in relatively barren environments without the opportunity to hide. Researchers have begun to study the impact of access to hiding spaces (“hides”) in farmed animals, including possible effects on animal welfare. The aims of this scoping review were to: 1) identify the farmed species that have been most used in research investigating the provision of hides, 2) describe the context in which hides have been provided to farmed animals, and 3) describe the impact (positive, negative or neutral/inconclusive) that hides have on animals, including indicators of animal welfare. Three online databases (CAB Abstracts, Web of Science, and PubMed) were used to search for a target population of farmed animals with access to hiding spaces. From this search, 4,631 citations were screened and 151 were included in the review. Fourteen animal types were represented, most commonly chickens (48% of papers), cattle (9%), foxes (8%), and fish (7%). Relatively few papers were found on other species including deer, quail, ducks, lobsters, turkeys, and goats. Hides were used in four contexts: at parturition or oviposition (56%), for general enrichment (43%), for neonatal animals (4%), or for sick or injured animals (1%). A total of 218 outcomes relevant to our objectives were found including 7 categories: hide use, motivation, and/or preference (47% of outcomes), behavioural indicators of affective state (17%), health, injuries, and/or production (16%), agonistic behaviour (8%), abnormal repetitive behaviours (6%), physiological indicators of stress (5%), and affiliative behaviours (1%). Hiding places resulted in 162 positive (74%), 14 negative (6%), and 42 neutral/inconclusive (19%) outcomes. Hides had a generally positive impact on the animals included in this review; more research is encouraged for under-represented species.

Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.

Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease. CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis) learns representations of molecular pixel profiles from multiplexed images that can be clustered to quantify subcellular landmarks and capture interpretable cellular phenotypes.