Biology subjects

Rittscher, J.

Publications and source records attributed to Rittscher, J..

15 recordsLinked to original sources

bioRxiv2026

SPC-Clean: A napari Plugin for Reducing Speckle and Isolated Pixel Noise in Fluorescence Microscopy Images

Speckle artifacts and isolated foreground pixels are common in fluorescence microscopy and can interfere with segmentation and subsequent quantitative image analysis. Conventional denoising methods often modify image intensities through filtering or smoothing, potentially altering biologically relevant fluorescence signals. We introduce Sparse Pixel Cluster Cleaning (SPC-Clean), a topology-aware method that removes poorly supported foreground pixels through iterative neighborhood analysis of a thresholded mask. SPC-Clean is deterministic, training-free, preserves original fluorescence intensities for practical microscopy workflows.

bioinformatics↗

bioRxiv2026

Scalable spatial DNA sequencing from archival tissue maps copy number subclones

Spatially resolved DNA sequencing holds promise due to its potential utility in understanding cancer intra-tumour heterogeneity and tumour evolution in relation to tissue architecture. However, it has so far been used to a limited extent due to technical challenges and high cost of existing methods. Hence we aimed to develop a high throughput spatial genomic assay to obtain copy number alteration (CNA) information at user-defined spatial resolution. We derived CNA profiles from ultra-low coverage whole genome sequencing at sub-millimetre resolution from archival samples using a novel method called Adaptive Resolution Multiscale Spatial DNA sequencing (ARMS DNAseq). We used it to profile CNAs from more than 766 regions (tiles) from 3 patients, covering a total area of over 300 mm2, with 1.2-2.6 million mapped reads per tile and tile sizes of 0.1-0.99mm2. Using ARMS DNAseq, we delineate tumour evolution in a spatial context, and identify more tumour subclones that were obscured or incompletely represented in bulk multi-region whole genome sequencing. Next, we show associations between tumour subclones and morphology, and prediction of subclone identity from deep learning-derived image representations. Finally, we demonstrate multi-omic integration by alignment with spatial transcriptomic data, showing subclone-specific immune cell co-occurrence as well as transcriptional programmes cutting across subclone boundaries. ARMS DNAseq converts low-throughput, region-by-region profiling into a scalable and adaptable workflow for direct spatial copy number profiling from archival tissue sections.

genomics↗

bioRxiv2026

Quantitative multi-scale morphodynamic analysis reveals ratchet-like collective DVE migration and epiblast retrograde cell flow during anterior patterning in the mouse embryo

The collective unidirectional migration of distal visceral endoderm (DVE) cells in the early mouse embryo is required to pattern the anterior-posterior (A-P) axis in the epiblast. It is unknown to what extent A-P axial asymmetries exist prior to DVE migration, how migration becomes channeled towards one side of the embryo, and whether the epiblast cells they migrate over have coordinated movements. We developed a quantitative embryo-wide, tissue-tracking approach to analyse visceral endoderm and epiblast tissue morphodynamics in a longitudinal light-sheet imaged, multi-embryo data-set. Here we show that asymmetric morphology of the ectoplacental cone already present prior to DVE migration correlates with the alignment of the A-P axis but not its polarity. DVE cell movements are initiated with a relatively low cell coordination and small net migration, then get channelled in an abrupt transition to a highly coordinated, uni-directional anterior motion. This anteriorwards migration is characterised by a ratchet-like, intermittent motion. Vertex modelling demonstrates that tissue rheology can account for DVE start-stop motion, and suggests that T1-mediated stress relaxation in the surrounding tissue can facilitate intermittent DVE motion without requiring intrinsic fluctuations in DVE velocity. Finally, comparing cell movement of the DVE with the underlying epiblast reveals a previously unknown coordinated motion in the anterior epiblast, opposite to the direction of DVE migration. Together these data provide insights into the origin of embryonic axial asymmetry and a previously unappreciated coordination between VE and epiblast tissue-motion during anterior-posterior patterning. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/720339v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1cfe4fdorg.highwire.dtl.DTLVardef@1c2c3e6org.highwire.dtl.DTLVardef@1cb6a5corg.highwire.dtl.DTLVardef@1b3ed19_HPS_FORMAT_FIGEXP M_FIG C_FIG See supplemental movie: animated abstract

developmental biology↗

bioRxiv2025

Redefining the topology of the human bone marrow using augmented spatial transcriptomic analysis

The bone marrow (BM) is the main site of haematopoiesis in adult life. Our understanding of the pathogenesis of BM-derived blood cancers is limited by lack of spatial contextualisation. While emerging spatial transcriptomic (ST) platforms offer unprecedented opportunities for spatially-resolved cellular phenotyping, we recognise and incorporate the power of AI-based tissue feature detection to enhance ST workflows. We perform ST analysis to define the topology of the normal bone marrow (BM) and BM in myeloproliferative neoplasms (MPNs), profiling 5,104,452 cells across 30 human BM samples. Following rigorous histology-based QC, we identify spatially-restricted trajectories of haematopoiesis and extend our understanding of the haematopoietic stem cell (HSC) niche. We find that BM fibrosis in MPN is associated with expansion of distinct immune and stromal co-enriched cell neighbourhoods. We then present a machine learning (ML)-based model trained on ST data that quantifies BM microenvironmental deviation, identifying heretofore unrecognised inter- and intra-individual sample heterogeneity in MPN. Our study demonstrates the potential for AI-based augmented ST analysis, and redefines our understanding of human BM topology.

cancer biology↗

bioRxiv2025

Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale

Ultrastructural changes of mitochondria are closely associated with metabolic dysfunction, leading to a variety of human disorders. These changes can be visualized by pan-Expansion Microscopy (pan-ExM), a 3D microscopy technique requiring only standard fluorescence microscopes, at a throughput exceeding that of the current gold standard, 3D electron microscopy, by orders of magnitude. However, a lack of tools that enable the characterization and quantification of the observed ultrastructural features in the acquired 3D datasets at a comparable throughput has hindered the widespread adoption of pan-ExM for quantitative imaging. Here, we present an automated deep-learning based segmentation approach that utilizes pan-ExMs power to acquire multi-channel images and uses specific labeling as the annotation for the training of the segmentation network. This molecular annotation reduces the required manual annotation effort for mitochondria substructures to just a few hours when setting up the experiment and thereby provides access to 3D suborganellar morphology of mitochondria at an unprecedented throughput. Our approach, which we term MAPS (Mitochondrial Automated Pan-ExM Segmentation), enables for the first time to quantify mitochondrial ultrastructural morphology at scale. We demonstrate this power by characterizing the 3D mitochondrial morphology at the organelle and sub-organelle level in tens of HeLa cells under different treatments and localizing mitochondrial proteins in the sub-organellar context. To demonstrate our technology in tissue, we compare the ultrastructural morphology of mitochondria in proximal tubules of kidneys of mice exhibiting acute kidney injury (AKI) with those of untreated mice, revealing striking differences in their cristae structure. MAPS can easily be adapted to different cell and tissue types, allowing the analysis of tens of samples per day, and therefore provides a versatile tool for a comprehensive understanding of mitochondrial ultrastructural changes in many disease contexts. Requiring only standard fluorescence microscopes and computer infrastructure, MAPS is readily adoptable by any lab. HighlightsO_LIMAPS utilizes specific labeling to train a segmentation model for 3D super-resolution pan-ExM images C_LIO_LISuborganellar mitochondrial features and their changes in disease models are quantified at high throughput C_LIO_LI3D protein distributions are correlated to ultrastructural features in mitochondria C_LIO_LIRequiring only standard lab infrastructure, MAPS is readily adoptable C_LI

cell biology↗

bioRxiv2024

Tracking exocytic vesicle movements with MSP-tracker/viewer reveals the spatial control of secretion in epithelial cells.

Understanding how specific secretory cargoes are targeted to distinct domains of the plasma membrane in epithelial cells requires analysing the trafficking of post-Golgi vesicles to their sites of secretion. We used the RUSH (retention using selective hooks) system to synchronously release an apical cargo, Cadherin 99C (Cad99C), and a basolateral cargo, the ECM protein Nidogen, from the endoplasmic reticulum and follow their movements to the plasma membrane. We also developed an interactive vesicle tracking framework, MSP-tracker and viewer, that exploits developments in computer vision and deep learning to determine vesicle trajectories in a noisy environment without the need for extensive training data. MSP-tracker outperformed other tracking software in detecting and tracking post-Golgi vesicles, revealing that Cad99c vesicles predominantly move apically with a mean speed of 1.1{micro}m/sec. This is reduced to 0.85 {micro}m/sec by a dominant slow dynein mutant, demonstrating that dynein transports Cad99C vesicles to the apical cortex. Furthermore, both the dynein mutant and microtubule depolymerisation cause lateral Cad99C secretion. Thus, microtubule organisation plays a central role in targeting apical secretion, suggesting that Drosophila does not have distinct apical versus basolateral vesicle fusion machinery. Nidogen vesicles undergo planar-polarised transport to the leading edge of follicle cells as they migrate over the ECM, whereas most Collagen is secreted at trailing edges. The follicle cells therefore bias secretion of different ECM components to opposite sides of the cell, revealing that the secretory pathway is more spatially organised than previously thought.

cell biology↗

bioRxiv2023

Single-cell phenomics reveals behavioural and mechanical heterogeneities underpinning collective migration during mouse anterior patterning

Distal Visceral Endoderm (DVE) cells show a stereotypic unidirectional migration essential for correct orientation of the anterior-posterior axis. They migrate within a simple epithelium, the Visceral Endoderm (VE). It is unknown how DVE cells negotiate their way amongst the surrounding VE cells, what determines the bounds of DVE migration within the VE, and the relative contributions of different cell behaviours to this migration. To address these questions, we used lightsheet microscopy to generate a multi-embryo, singlecell resolution, longitudinal dataset of cell behaviour and morphology. We developed a machine learning based pipeline to segment cells and a data-informed systematic computational framework to extract and compare select morphological, behavioural and molecular parameters of all VE cells in a unified coordinate space. Unbiased clustering of this single-cell phenomic dataset reveals considerable patterned phenotypic heterogeneity within the VE and a previously unknown sub-grouping within the DVE. While migrating, DVE cells retain regular morphology, do not exchange neighbours and are crowded, all hallmarks of the jammed state. In contrast, VE cells immediately ahead of them deform and undergo neighbour exchange. We show that DVE cells are characterised by higher levels of apical F-actin and elevated tension relative to the VE cells immediately ahead of them through which they migrate, but stop migrating upon reaching a region of the VE with matching elevated tension. Lefty1 mutants, known to show abnormal over-migration of DVE cells, show disruption to this patterned tension in the VE. Our findings provide novel insights into the control of cell behaviour during the remodelling of curved epithelia, indicating that the collective migration of sub-sets of cells can be circumscribed by modulating the mechanical properties of surrounding cells and that migrating cells in this context remain as a jammed solid flock, with surrounding cells facilitating their movement by becoming unjammed. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/534937v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@10a7153org.highwire.dtl.DTLVardef@963a1forg.highwire.dtl.DTLVardef@c1fcd0org.highwire.dtl.DTLVardef@1bcdded_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2021

Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks

White adipose tissue (WAT) plays a central role in metabolism, with multiple diseases and genetic mutations causing its remodeling. Quantitative analysis of white adipocyte size is of great interest to understand physiology and disease, but previous studies of H&E histology have been limited to a subsample of whole depot cross-sections. In this paper, we present the deep learning pipeline DeepCytometer, that can segment mouse and human whole slides ([~=]40,000 cells per mouse slide on average) using an adaptive tiling method, correct for cell overlap and reject non-white adipocytes from the segmentation. Using quantile colour maps we show intra- and inter-depot cell size heterogeneity with local correlation; quantile estimates also suggest significant differences in population estimates from 75 whole slides compared to smaller data sets. We propose three linked levels (body weight BW, depot weight DW and cell area quartiles) for exploratory analysis of mouse Klf14 phenotypes in gonadal and subcutaneous depots. We find a rich set of phenotypes when stratifying by sex, depot and three genotype strata: (1) WTs/Hets with a Het father (Controls), (2) WTs with a Het mother, and (3) Hets with a Het mother (functional KOs or FKOs). Namely, at BW level, mean difference testing suggests that female FKOs are similar to Controls, but WTs with a Het mother are significantly larger. At DW and cell levels, linear models with interaction terms and BW or DW covariates, respectively, reveal phenotypes not shown by difference of means tests. For example, at DW level, gonadal and subcutaneous models are similar, and female FKOs have lower fat percentage than Controls due to both an offset and the DW/BW slope in the linear model. Meanwhile, female WTs with a Het mother have on average similar fat percentage to Controls, but as their slopes are close to zero, their DWs are uncorrelated to BW, suggesting that larger female WTs with a Het mother have lower fat percentage than smaller ones. In contrast to depot level, at cell level female gonadal phenotypes diverge from subcutaneous ones. Furthermore, male Controls and FKOs have similar average area values in subcutaneous depots, but area~DW slope flattening in FKOs suggests that larger DWs could be caused by cell size increase in Controls and by cell count increase in FKOs. Thus, DeepCytometer and associated exploratory analysis reveal new insights into adipocyte heterogeneity and phenotyping.

bioinformatics↗

bioRxiv2021

TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

Single Particle Tracking (SPT) is one of the most widespread techniques to evaluate particle mobility in a variety of situations, such as in cellular and model membrane dynamics. The proposed TRAIT2D Python library is developed to provide object tracking, trajectory analysis and produce simulated datasets with graphical user interface. The tool allows advanced users to customise the analysis to their requirements. Availability and implementation: the software has been coded in Python, and can be accessed from: https://github.com/Eggeling-Lab-Microscope-Software/TRAIT2D, or the pypi and condaforge repositories. A comprehensive user guide is provided at https://eggeling-lab-microscope-software.github.io/TRAIT2D/.

bioinformatics↗

bioRxiv2020

Tumor irradiation combined with vascular-targeted photodynamic therapy enhances anti-tumor effects in preclinical prostate cancer.

RationaleThere is an important clinical need to improve the treatment of high risk localized and locally advanced prostate cancer (PCa), and to reduce the side effects of these treatments. We hypothesized that multi-modality therapy combining radiotherapy and vascular-targeted photodynamic therapy (VTP) could PCa tumour control compared against monotherapy with each of these treatments alone. This could provide proof-of-concept to take to the clinic. VTP is a focal therapy for localized PCa, which rapidly destroys targeted tumors through vascular disruption. Tumor vasculature is characterized by vessel immaturity, increased permeability, aberrant branching and inefficient flow. Fractionated radiotherapy (FRT) alters the tumor microenvironment and promotes transient vascular normalization. ObjectiveWe investigated whether sequential delivery of FRT followed by VTP 7 days later improves PCa tumor control compared to monotherapy with FRT or VTP alone. FindingsFRT induced vascular normalization changes in PCa flank tumor allografts, improving vascular function as demonstrated using dynamic contrast enhanced magnetic resonance imaging. FRT followed by VTP significantly delayed tumor growth in flank PCa allograft pre-clinical models, compared with monotherapy with FRT or VTP alone, and improved overall survival. ConclusionTaken together, these results suggest that combining FRT and VTP could become a promising multimodal clinical strategy in PCa therapy. This provides proof-of-concept for this multi-modality therapy approach to take forward to early phase clinical trials.

cancer biology↗

bioRxiv2019

Improving the diagnosis and classification of Ph-negative myeloproliferative neoplasms through deep phenotyping

Myeloproliferative neoplasms (MPNs) are clonal disorders characterized by excessive proliferation of myeloid lineages. Accurate classification and appropriate management of MPNs requires integration of clinical, morphological and genetic findings. Despite major advances in understanding the molecular and genetic basis, morphological assessment of the bone marrow trephine (BMT) remains paramount in differentiating between MPN subtypes and reactive conditions. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative and poorly reproducible criteria. To address this, we have developed a machine-learning strategy for the automated identification and quantitative analysis of megakaryocyte morphology using clinical BMT samples. Using a sample cohort of recently diagnosed or established ET (n = 48) and reactive control cases (n = 42) we demonstrated a high predictive accuracy (AUC = 0.95) of automated tissue ET diagnosis based upon these specific megakaryocyte phenotypes. These separate morphological phenotypes showed evidence of specific genotype associations, which offers promise that an automated cell phenotyping approach may be of clinical diagnostic utility as an adjunct to standard genetic and molecular tests. This has great potential to assist in the routine assessment of newly diagnosed or suspected MPN patients and those undergoing treatment / clinical follow-up. The extraction of quantitative morphological data from BMT sections will also have value in the assessment of new therapeutic strategies directed towards the bone marrow microenvironment and can provide clinicians and researchers with objective, quantitative data without significant demands upon current routine specimen workflows.

pathology↗

bioRxiv2019

KDML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens

Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad-hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge-Driven Machine Learning (KDML), a framework that systematically predicts multiple functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As proof of concept, we test KDML on three datasets describing phenotypes at the molecular, cellular and population levels, and show that it outperforms traditional analysis pipelines. In particular, KDML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors and TGF{beta} and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcome. These results highlight KDML as a systematic framework for discovering novel scale-crossing and clinically relevant gene functions. KDML is highly generalizable and applicable to various large-scale genetic perturbation screens.

systems biology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗

bioRxiv2018

Identification of C elegans strains using a fully convolutional neural network on behavioural dynamics

The nematode C. elegans is a promising model organism to understand the genetic basis of behaviour due to its anatomical simplicity. In this work, we present a deep learning model capable of discerning genetically diverse strains based only on their recorded spontaneous activity, and explore how its performance changes as different embeddings are used as input. The model outperforms hand-crafted features on strain classification when trained directly on time series of worm postures.

animal behavior and cognition↗

bioRxiv2018

Motion Sensing Superpixels (MOSES): A systematic framework to quantify and discover cellular motion phenotypes

Cellular motion is fundamental in tissue development and homeostasis. There is strong interest in identifying factors that affect the interactions of cells in disease but analytical tools for robust and sensitive quantification in varying experimental conditions for large extended timelapse acquisitions is limited. We present Motion Sensing Superpixels (MOSES), a method to systematically capture diverse features of cellular dynamics. We quantify dynamic interactions between epithelial cell sheets using cell lines of the squamous and columnar epithelia in human normal esophagus, Barretts esophagus and esophageal adenocarcinoma and find unique boundary formation between squamous and columnar cells. MOSES also measured subtle changes in the boundary formation caused by external stimuli. The same conclusions of the 190 videos were arrived at unbiasedly with little prior knowledge using a visual motion map generated from unique MOSES motion signatures. MOSES is a versatile framework to measure, characterise and phenotype cellular interactions for high-content screens.

bioinformatics↗