Biology subjects

Moor, A.

Publications and source records attributed to Moor, A..

5 recordsLinked to original sources

bioRxiv2026

SubCellSpace: Automated characterization of subcellular mRNA localization patterns in spatial transcriptomics

The localized translation of transcripts is a universal phenomenon across biological domains. Many examples of subcellular RNA localization and their functional importance have been described. However, these examples remain anecdotal, and a more systematic genome and cell-type-wide analysis is needed. Current spatial transcriptomic techniques can characterize hundreds to thousands of transcript species at subcellular resolutions, enabling the large-scale investigation of subcellular mRNA localization. Here we describe SubCellSpace, a computational framework to learn general representations of mRNA localization patterns. By embedding observed single-cell subcellular localization patterns (SLPs) to an interpretable latent space, SubCellSpace can detect and statistically infer the presence of SLPs, uncover colocalizing gene-pairs and characterize cellular heterogeneity for pattern-presentation. We benchmark SubCellSpace in both synthetic and real data, showing it can correctly detect previously described apical/basal polarized genes in the enterocytes of mouse small-intestine, as well as encode the enterocytes orientation. Additionally, we provide a tailored spatial transcriptomics validation dataset for benchmarking SLP identification based on transcripts previously described to be enriched near subcellular structures in HEK293T cells. We propose a practical and computationally-efficient classification workflow that automatically detects localized transcript species and quantifies their degree of patterning, while controlling false positive rates. Finally, we showcase SubCellSpace in both supervised and unsupervised settings, to either classify pre-determined SLPs or to explore spatial patterning without specifying pattern types a priori. Automated AI models such as SubCellSpace and their integration in spatial transcriptomics analysis workflows will help characterize previously undiscovered subcellular RNA localization phenomena, providing novel insights into post-transcriptional regulation mechanisms.

bioinformatics↗

bioRxiv2025

Memory CD4 T cells orchestrate neoadjuvant-responsive niches in colorectal cancer liver metastases

Colorectal cancer frequently progresses to liver metastases (CRLM), a stage with limited treatment options and poor prognosis. Neoadjuvant chemotherapy is used to control tumor growth and enable resection, yet many patients fail to respond, and the mechanisms underlying this variability remain unclear. To identify determinants of treatment response, we profiled T cell states and their spatial organization in CRLM. We found that spatial arrangement and polarization of CD4 memory T cell networks determine treatment outcome. In responders, Th1-like CD4 memory T cells organized with effector-memory CD8 T cells and antigen-presenting cells (APCs) into therapy-responsive immune niches (TRINs) that support CD4-mediated APC licensing and local immune engagement. Non-responders lacked such immune architecture, exhibiting myeloid-rich regions dominated by circulating-like CD4 memory and regulatory T cells. CD4-driven TRINs thus emerge as key determinants of chemotherapy efficacy and provide a rationale for developing biomarkers and strategies that enhance CD4-APC-CD8 crosstalk within organized immune niches. Statement of significanceTh1-polarized CD4 memory T cells form therapy-responsive immune niches (TRINs) that orchestrate CD4, CD8, and APC function in colorectal cancer liver metastases, a clinically challenging and immunologically cold tumor type. TRINs define chemotherapy response and provide a mechanistic foundation for biomarker development and immunotherapy strategies designed to restore anti-tumor immunity.

immunology↗

bioRxiv2024

DemoTape: Computational demultiplexing of targeted single-cell sequencing data

BackgroundSingle-cell sequencing can provide novel insights into the understanding and treatment of diseases. In cancer, for example, intratumor heterogeneity is a major cause of treatment resistance and relapse. Although technological progress has substantially increased the throughput of sequenced cells, single-cell sequencing remains cost and labor-intensive. Multiplexing, i.e., the pooling and subsequent joint preparation and sequencing of samples, followed by a demultiplexing step, is a common practice to reduce expenses and confounding batch effects, especially in single-cell RNA sequencing. ResultsHere, we introduce demoTape, a computational demultiplexing method for targeted single-cell DNA sequencing (scDNA-seq) data based on a distance metric between individual cells at single-nucleotide polymorphisms loci. To validate demoTape, we sequence three B-cell lymphoma patients separately and multiplexed on the Tapestri platform. We find similar genotypes, clones, and evolutionary histories in all three samples when comparing the individual with the demultiplexed samples. Using the three individually sequenced samples, we simulate multiplexed ground truth data and show that demoTape outperforms state-of-the-art demultiplexing methods designed for RNA sequencing data. Additionally, we demonstrate through downsampling that the inferred clonal composition remained largely stable for samples with fewer cells despite the inevitable loss in resolution of low-frequency clones. ConclusionsMultiplexing and subsequent genotype-based demultiplexing of scDNA-seq will reduce costs and workload, eventually allowing the sequencing of more samples. This will open new possibilities and accelerate the investigation of biological questions where cellular heterogeneity on the genomic level plays a crucial role.

bioinformatics↗

bioRxiv2021

CD8+ T cell signature in acute SARS-CoV-2 infection identifies memory precursors

Immunological memory is a hallmark of adaptive immunity and facilitates an accelerated and enhanced immune response upon re-infection with the same pathogen1,2. Since the outbreak of the ongoing coronavirus disease 19 (COVID-19) pandemic, a key question has focused on whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cells stimulated during acute infection give rise to long-lived memory T cells3. Using spectral flow cytometry combined with cellular indexing of transcriptomes and T cell receptor (TCR) sequencing we longitudinally characterize individual SARS-CoV-2-specific CD8+ T cells of COVID-19 patients from acute infection to one year into recovery and find a distinct signature identifying long-lived memory CD8+ T cells. SARS-CoV-2-specific memory CD8+ T cells persisting one year after acute infection re-express CD45RA and interleukin-7 receptor (CD127), upregulate T cell factor-1 (TCF1), and maintain low CCR7, thus resembling CD45RA+ effector-memory T (TEMRA) cells. Tracking individual clones of SARS-CoV-2-specific CD8+ T cells, we reveal that an interferon signature marks clones giving rise to long-lived cells, whereas prolonged proliferation and mammalian target of rapamycin (mTOR) signaling are associated with clone contraction and disappearance. Collectively, we identify a transcriptional signature differentiating short-from long-lived memory CD8+ T cells following an acute virus infection in humans.

immunology↗

bioRxiv2020

Conventional NK cells and tissue-resident ILC1s join forces to control liver metastasis

The liver is a major metastatic target organ, and little is known about the role of immunity in controlling hepatic metastases. Here, we discovered that the concerted and non-redundant action of two innate lymphocyte subpopulations, conventional NK cells (cNKs) and tissue-resident type I Innate Lymphoid Cells (trILC1s), is essential for anti-metastatic defense. Using different preclinical models for liver metastasis, we found that trILC1 control metastatic seeding, whereas cNKs restrain outgrowth. The antimetastatic activity of cNKs is regulated in a tumor type-specific fashion. Thereby, individual cancer cell lines orchestrate the emergence of cNK subsets with unique phenotypic and functional traits. Understanding cancer-cell- as well as innate-cell-intrinsic factors will allow the exploitation of hepatic innate cells for development of novel cancer therapies. SignificanceInnate lymphoid cells hold great promise for the treatment of metastases. Development of effective therapies based on these versatile immune cells, however, is hampered by our limited knowledge of their behavior in the metastatic niche. Here, we describe that defense against liver metastasis requires the collaboration between two innate lymphocyte subsets, conventional NK cells (cNKs) and tissue-resident type I innate lymphoid cells (trILC1s). We show that different cancers generate their own particular metastatic niche inducing specific changes in cNKs and trILC1s. Further, we uncover specific cNK subsets that can be manipulated to improve their anti-metastatic potential. Our work contributes to understanding how cancer-specific factors and hepatic innate lymphocytes exert mutual influence and how this can be exploited for therapeutic purposes. HighlightsO_LIcNKs and trILC1s collaborate to control hepatic metastasis C_LIO_LItrILC1s restrict seeding and cNKs control outgrowth of cancer cells in the liver C_LIO_LIIndividual cancer cell lines orchestrate a distinct metastatic niche C_LIO_LIThe metastatic niche dictates the phenotype and function of cNKs C_LI

immunology↗