Biology subjects

Borm, L. E.

Publications and source records attributed to Borm, L. E..

6 recordsLinked to original sources

bioRxiv2026

On technical biases in imaging spatial transcriptomics

Imaging spatial transcriptomics (iST) has revolutionized our ability to contextualize gene expression within intact tissue architectures at single-cell and subcellular resolution. However, these platforms introduce complex technical biases that span the entire measurement pipeline -- from sample preparation to data acquisition to quantification. Here, we categorize these biases into experimental (e.g., sample preservation and spatial sampling) and computational sources (e.g., z-stack registration and field-of-view stitching). Using public datasets, we demonstrate how different biases can compound and manifest, and propose a unified count modeling framework to formalize these sources of variation. Finally, we call for a more integrated understanding of iST data origins and greater knowledge sharing within and across sectors to support the prospective development of more robust experimental design and reliable downstream analysis.

bioinformatics↗

bioRxiv2023

Glioblastoma is spatially organized by neurodevelopmental programs and a glial-like wound healing response

Glioblastoma is the deadliest brain cancer, characterized by large cellular diversity whose complexity and organizing principles are only starting to be uncovered. Both neurodevelopment-like and mesenchymal-like cell states have been described in glioblastoma1-8, with the latter being strongly implicated in malignancy and disease progression8-11. However, the nature of these mesenchymal-like cell states remains unresolved. Here, we performed deep single-cell RNA sequencing of rare glioblastoma cases where tissue could be sampled from tumor core to macroscopically normal cortex. We discovered that previously defined mesenchymal-like tumor cell states instead represented a wound response that was shared across both malignant and non-malignant cell types and was spatially confined to the tumor bulk. Using glioblastoma organoids, we showed that the wound response transcriptional state could be reversibly induced in vitro by hypoxia and human plasma. We used multiplex single-molecule spatial transcriptomics12 on a large patient cohort to show that the activation of wound response states was associated with hypoxia, and organized by distance to perivascular niches. Our findings help reconceptualize the cellular landscape of glioblastoma, wherein a reactive wound-response tissue state shared by all cells in the tumor bulk is superimposed on a fundamentally neurodevelopmental and glial tumor.

cancer biology↗

bioRxiv2022

Comprehensive cell atlas of the first-trimester developing human brain

The adult human brain likely comprises more than a thousand kinds of neurons, and an unknown number of glial cell types, but how cellular diversity arises during early brain development is not known. Here, in order to reveal the precise sequence of events during early brain development, we used single-cell RNA sequencing and spatial transcriptomics to uncover cell states and trajectories in human brains at 5 - 14 post-conceptional weeks (p.c.w.). We identified twelve major classes and over 600 distinct cell states, which mapped to precise spatial anatomical domains at 5 p.c.w. We uncovered detailed differentiation trajectories of the human forebrain, and a surprisingly large number of region-specific glioblasts maturing into distinct pre-astrocytes and pre-oligodendrocyte precursor cells (pre-OPCs). Our findings reveal the emergence of cell types during the critical first trimester of human brain development.

developmental biology↗

bioRxiv2020

Spatial tissue profiling by imaging-free molecular tomography

Genomics techniques are currently being adapted to provide spatially resolved omics profiling. However, the adaptation of each new method typically requires the setup of specific detection strategies or specialized instrumentation. A generic approach to spatially resolve different types of high throughput data is missing. Here, we describe an imaging-free framework to localize high throughput readouts within a tissue by combining compressive sampling and image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (STRP-seq) and validated this method with a transcriptome profiling of the murine brain. To verify the broad applicability of STRP-seq, we applied the technique on the brain of the Australian bearded dragon Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. Overall, the proposed framework constitutes a new approach that allows upgrading in a generic fashion conventional genomic assays to spatially resolved techniques.

genomics↗

bioRxiv2019

Segmentation-free inference of cell-types from in situ transcriptomics data

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

bioinformatics↗

bioRxiv2018

Spatial organization of the somatosensory cortex revealed by cyclic smFISH

The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.

neuroscience↗