Biology subjects

Geiger, T.

Publications and source records attributed to Geiger, T..

13 recordsLinked to original sources

bioRxiv2026

Vesicular pseudopodia define the fusion site on large secretory vesicles of the Drosophila salivary glands

Large secretory vesicles (LSVs) pose a scaling problem for regulated exocytosis. Their micron-scale dimensions greatly increase the vesicular membrane surface area, making productive engagement between the vesicular and target membrane fusion machinery unlikely. Here, we show that vesicular pseudopodia define the fusion sites of LSVs in Drosophila larval salivary glands. Focused ion beam scanning electron microscopy revealed that most LSVs project polarized pseudopodia that interconnect neighboring vesicles and orient toward the apical membrane. Exposed pseudopodia were frequently observed at the apical surface and associated with narrow fusion pores, indicating that fusion occurs at these structures. Three-dimensional correlative light and electron microscopy showed that the I-BAR protein Missing in Metastasis (MIM) selectively localizes to exposed pseudopodia. Proteomic analysis based on a MIM pull-down assay identified exocyst components, including Sec15, which localizes to pseudopodia and persists at fusion sites throughout secretion. Finally, the tetraspanin Tsp42Ee marked complementary apical fusion domains and was required for efficient exocytosis. Our findings support a model in which prepatterned vesicular and apical membrane domains coordinate efficient exocytosis. SummaryRegulated exocytosis of large secretory vesicles is facilitated by vesicular pseudopodia and an apical fusion domain that spatially organizes membrane tethering and fusion during secretion.

cell biology↗

bioRxiv2025

A Novel Algorithm for the Harmonization of Pan-cancer Proteomics

Proteomic characterization of cancer tissues holds the potential to advance therapeutic options and reveal novel biomarkers by unlocking insights available only on the proteome level. However, proteomics data analysis is greatly challenged by systematic technical variability in experimental protocols, instrumentation and data processing, restricting comparisons between studies. With the continued and unprecedented growth of proteomics datasets, a comprehensive strategy for harmonizing these datasets is necessary to enable large-scale integrative analyses. Herein, we describe a novel framework for pan-cancer harmonization and imputation, which offers the scientific community an updated approach to this challenge. Rather than relying on a single batch-effect correction algorithm, our multi-step approach accurately addresses critical systematic differences with custom-tailored solutions, including standardized reanalysis of raw data and an autoencoder for pan-cancer integration. By introducing a suite of benchmarks, we bridge the critical gap in reliable harmonization evaluation. Using this framework, we created a harmonized pan-cancer dataset and demonstrated its superiority over existing solutions and previous pan-cancer harmonization efforts. We further demonstrated its utility in revealing prognostic markers, estimating indication-wide biomarker prevalence, and facilitating target discovery for cancer subtypes. We expect our work to provide powerful tools supporting proteomics research for precision cancer medicine.

bioinformatics↗

bioRxiv2024

High throughput single-cell proteomics of in-vivo cells

Single-cell mass spectrometry-based proteomics (SCP) can resolve cellular heterogeneity in complex biological systems and provide a system-level view of the proteome of each cell. Major advancements in SCP methodologies have been introduced in recent years, providing highly sensitive sample preparation methods and mass spectrometric technologies. However, most studies present limited throughput and mainly focus on the analysis of cultured cells. To enhance the depth, accuracy, and throughput of SCP for tumor analysis, we developed an automated, high-throughput pipeline that enables the analysis of 1,536 single cells in a single experiment. This approach integrates low-volume sample preparation, automated sample purification, and LC-MS analysis with the Slice-PASEF method. Integration of these methodologies into a streamlined pipeline led to a robust and reproducible identification of more than 3000 proteins per cell. We applied this pipeline to analyze tumor macrophages in a murine lung metastasis model. We identified over 1,800 proteins per cell, including key macrophage markers and [~]500 differentially expressed proteins between tumor and control macrophages. PCA analysis successfully separated these populations, revealing the utility of SCP in capturing biologically relevant signals in the tumor microenvironment. Our results demonstrate a robust and scalable pipeline poised to advance single-cell proteomics in cancer research.

biochemistry↗

bioRxiv2023

Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation

Emerging evidence reinforce the role of non-genetic adaptive resistance to chemotherapy, that involves rewiring of transcriptional programs in surviving tumors. We combined longitudinal transcriptomics with temporal pattern analysis to dissect patient-specific emergence of resistance in breast cancer. Matched triplets of tumor biopsies (pre-treatment, post-treatment and adjacent normal) were collected from breast cancer patients who received neo-adjuvant chemotherapy. Full transcriptome was analyzed by longitudinal pattern classification to follow patient-specific expression modulations. We found that dynamics of gene expression dictates resistance-related modulations. The results unraveled important principles in emergence of adaptive resistance: 1. Genes with resistance patterns are already dysregulated in the primary tumor, supporting a primed drug-tolerant state. 2. In each patient, multiple resistance-related genes are rewired but converge into few dysregulated modules. 3. Rewiring of diverse genes and pathway dysregulation vary among individuals who receive the same treatments. Patient-specific chemoresistome maps disclosed tumors acquired resistance and exposed their vulnerabilities. Mapping the complexity of dysregulated pathways in individual patients revealed important insights on adaptive resistance mechanisms. To survive the toxic drug effect, tumor cells either sustain a drug-tolerant state or intensify it, specifically bypassing the drugs interference. Depicting an individual road map to resistance can offer personalized therapeutic strategies.

cancer biology↗

bioRxiv2022

Metabolic plasticity of T-cell therapies: multi-omic profiling of interacting human tumor-infiltrating lymphocytes and autologous tumor adoptive cell therapy

Adoptive cell therapy (ACT) based on ex vivo expanded autologous tumor-infiltrating lymphocytes (TILs) can mediate durable antitumor responses even in heavily pretreated patients. However, only a subset of patients responds to ACT; efforts to identify correlates of response have focused on profiling the tumor or the TIL but rarely in an interactive environment. Interactive profiling can provide unique insights into the clinical performance of TILs since the fate, function, and metabolism of TILs are influenced by autologous tumor-derived factors. Here, we performed a suite of cell-sparing assays dubbed holistic analysis of the bioactivity of interacting T cells and autologous tumor cells (HABITAT). HABITAT profiling of TILs used for human ACT and their autologous tumor cells included function-based single-cell profiling by timelapse imaging microscopy in nanowell grids (TIMING); multi-omics using RNA-sequencing and proteomics; metabolite inference using genome-scale metabolic modeling, and pulse-chase assays based on confocal microscopy to profile the uptake and fate of fatty acids (FA). Phenotypically, the ACT TILs from both responders (Rs) and nonresponders (NRs) were comprised of predominantly effector memory T cells (TEM cells) and did not express a high frequency of programmed death ligand-1 (PD-L1) and showed no differences in TCR diversity. Our results demonstrate that while tumor cells from both Rs and NRs are efficient at uptaking FAs, R TILs are significantly more efficient at utilizing FA through fatty acid oxidation (FAO) than NR TILs under nutrient starvation conditions. While it is likely that lipid and FA uptake is an inherent adaptation of TIL populations to lipid-rich environments, performing FAO sustains the survival of TILs and allows them to sustain antitumor cytolytic activity. We propose that metabolic plasticity enabling FAO is a desirable attribute of human TILs for ACT leading to clinical responses.

immunology↗

bioRxiv2022

S6K1 phosphorylates Cdk1 and MSH6 to regulate DNA repair.

The mTORC1 substrate, S6 Kinase 1 (S6K1), is involved in the regulation of cell growth, ribosome biogenesis, glucose homeostasis and adipogenesis. Accumulating evidence has suggested a role for mTORC1 signaling in the DNA damage response. This is mostly based on the findings that mTORC1 inhibitors sensitized cells to DNA damage. However, a direct role of the mTORC1-S6K1 signaling pathway in DNA repair and the mechanism by which this signaling pathway regulates DNA repair is unknown. In this study, we discovered a novel role for S6K1 in regulating DNA repair through the coordinated regulation of the cell cycle, homologous recombination (HR) DNA repair (HRR) and mismatch DNA repair (MMR) mechanisms. Here we show that S6K1 orchestrates DNA repair by phosphorylation of Cdk1 at serine 39, causing G2/M cell cycle arrest enabling homologous recombination and by phosphorylation of MSH6 at serine 309, enhancing MMR. Moreover, breast cancer cells harboring RPS6KB1 gene amplification show increased resistance to several DNA damaging agents and S6K1 expression is associated with poor survival of breast cancer patients treated with chemotherapy. Our findings reveal an unexpected function of S6K1 in the DNA repair pathway, serving as a tumorigenic barrier by safeguarding genomic stability.

cancer biology↗

bioRxiv2021

Proteomic landscape of multi-layered breast cancer internal tumor heterogeneity

Despite extensive research, internal tumor heterogeneity presents enormous challenges to achieve complete therapeutic responses. Changes in protein expression are central determinants of cancer phenotypes that reflect potential therapeutic targets. However, previous proteomic studies did not address internal heterogeneity, therefore, masked the necessary spatial resolution to achieve a comprehensive understanding of cancer complexity. Here we present the first large-scale multi-focal breast cancer proteomic study of 330 tumor regions which associated cancer cell function, pathological parameters, and spatial localization of each tumor region. We found marked internal proteomic heterogeneity even within tumors presenting homogeneous receptor expression. Additionally, analysis of the internal heterogeneity, based on coexisting receptor expression or histological patterns in single tumors, showed significant functional differences between homogeneous and heterogeneous tumors related to cancer metabolism, immunogenicity, and proliferation. We anticipate that this study will serve as a starting point towards the development of improved cancer therapy and diagnostics.

cancer biology↗

bioRxiv2020

Spontaneous regression of micro-metastases following primary tumor excision: a critical role for primary tumor secretome

Numerous case studies have reported spontaneous regression of recognized metastases following primary tumor (PT) excision, but underlying mechanisms are elusive. Here we present a model of metastases regression and latency following PT excision, and identify potential underlying mechanisms. Using MDA-MB-231HM human breast cancer cells that express highly sensitive luciferase, we were able to monitor early stages of spontaneous metastases development in BALB/c nu/nu mice. Removal of the PT caused marked regression of the smallest micro-metastases, but not of larger metastases, and in vivo supplementation of tumor secretome diminished this regression, suggesting that PT-secreted factors promote early metastatic growth. Correspondingly, cancer cell conditioned medium reduced apoptosis and enhanced MDA-MB-231HM adhesion in vitro. To identify specific mediating factors, cytokine array and proteomic analysis of MDA-MB-231HM secretome were conducted. Results identified significant enrichment of angiogenesis, growth factors binding and activity, focal adhesion, metalloprotease regulation, and apoptosis regulation processes. Simultaneous in vivo blockade of four secreted key potential mediators of these processes, IL-8, PDGFaa, Serpin E1 (PAI-1), and MIF, arrested development of micro-metastases in the presence of the PT. Interestingly, using the public TCGA provisional dataset, high protein levels of these four factors were correlated with poor survival in a cohort of lung adenocarcinoma patients. These results demonstrate regression and latency of micro-metastases following PT excision, and a crucial role for PT-secretome in promoting early metastatic stages in MDA-MB-231HM xenografts. If generalized, such findings can suggest novel approaches to control minimal residual disease during and following PT excision.

cancer biology↗

bioRxiv2020

Spatio-temporal Proteomic Analysis of Stress Granule disassembly using APEX Reveals Regulation by SUMOylation and links to ALS pathogenesis

Cytoplasmic stress granules (SG) form in response to a variety of cellular stresses by phase-separation of proteins associated with non-translating mRNAs. SG provide insight into the biology of neurodegeneration, including amyotrophic lateral sclerosis (ALS) because they approximate some of the molecular conditions for nucleation of insoluble aggregates in neuropathological inclusions. Whereas much has been learned about SG formation, a major gap remains in understanding the compositional changes SG undergo during normal disassembly and under disease conditions. Here, we address this gap by proteomic dissection of SG temporal disassembly sequence, using multi-bait APEX proximity-proteomics. We discover 109 novel SG-proteins and characterize at proteomic resolution two biophysically distinct SG substructures. We further demonstrate that dozens of additional proteins are recruited to SG specifically during disassembly, indicating that it is a highly regulated process. The involved proteins link SG disassembly, to mitochondrial biology and the cytoskeleton. Parallel analysis with C9ORF72-associated dipeptides, which are found in patients with ALS and frontotemporal dementia, demonstrated compositional changes in SG during the course of disassembly and focused our attention on the roles SUMOylation in SG disassembly. We demonstrate that broad SUMOylation of SG-proteins is required for SG disassembly and is impaired by C9ORF72-associated dipeptides, representing an unexplored potential molecular mechanism of neurodegeneration. Altogether, out study fundamentally increases the knowledge about SG composition in human cells by dissecting the SG spatio-temporal proteomic landscape, provides an in-depth resource for future work on SG function and reveals basic and disease-relevant mechanisms of SG disassembly. HighlightsO_LIMulti bait APEX proximity labelling reveals 109 novel SG proteins and two distinct SG substructures. C_LIO_LIProteomic dissection of SG temporal disassembly under basal conditions and with a model of neurodegeneration. C_LIO_LIDisassembly-engaged proteins (DEPs) include SUMO ligases that are recruited during normal disassembly and dysregulated in ALS-like conditions. C_LIO_LIPervasive SG protein SUMOylation during SG disassembly is impaired by ALS-like conditions. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=168 SRC="FIGDIR/small/830133v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@15899daorg.highwire.dtl.DTLVardef@1dcd399org.highwire.dtl.DTLVardef@c7ef12org.highwire.dtl.DTLVardef@20c096_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

Multidimensional extracellular-matrix analysis reveals a silent pre-clinical signature in inflammatory disease

Identification of early processes leading to complex tissue pathologies, such as inflammatory bowel diseases, poses a major scientific and clinical challenge that is imperative for improved diagnosis and treatment. Most studies of inflammation onset focus on cellular processes and signaling molecules, while overlooking the environment in which they take place, the continuously remodeled extracellular matrix. In this study, we used colitis models for investigating extracellular-matrix dynamics during disease onset, while treating the matrix as a complete and defined entity. Through the analysis of matrix structure, stiffness and composition, we unexpectedly revealed that even prior to the first clinical symptoms, the colon displays its own unique extracellular-matrix signature and found specific markers of clinical potential, which were also validated in human subjects. We also show that the emergence of this pre-symptomatic matrix is mediated by sub-clinical infiltration of neutrophils and monocytes bearing remodeling enzymes. Remarkably, whether the inflammation is chronic or acute, its matrix signature converges at pre-symptomatic states. We suggest that the existence of a pre-symptomatic extracellular-matrix is general and relevant to a wide range of diseases.

systems biology↗

bioRxiv2018

Proteomic and Genomic Signatures of Repeat-instability in Cancer and Adjacent Normal Tissues

Repetitive sequences are hotspots of evolution at multiple levels. However, due to technical difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content of proteomes and genomes, directly from proteomic and genomic raw sequence data, and applied it to analyze a wide range of tumors and normal tissues. We identify high similarity between the repeat-instability in tumors and their patient-matched normal tissues, but also tumor-specific signatures, both in protein expression and in the genome, that strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We find an inverse relationship between repeat-instability and point mutation load, within and across patients, and independently of other somatic aberrations. Thus, repeat-instability is a distinct, transient and compensatory adaptive mechanism in tumor evolution.

cancer biology↗

bioRxiv2018

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

cancer biology↗

bioRxiv2018

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors

Translation errors limit the accuracy of information transmission from DNA to proteins. Selective pressures shape the way cells produce their proteins: the translation machinery and the mRNA sequences it decodes co-evolved to ensure that translation proceeds fast and accurately in a wide range of environmental conditions. Our understanding of the causes of amino acid misincorporations and of their effect on the evolution of protein sequences is largely hindered by the lack of experimental methods to observe errors at the full proteome level. Here, we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, we identify E. coli and S. cerevisiae peptides whose mass and fragment ion spectrum are consistent with that of a peptide bearing a single amino acid substitution, and verify that such spectrum cannot result from a post-translational modification. Our analyses confirm that most substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in bacteria and yeast, suggesting that the error spectrum is chemically constrained. Treating E. coli cells with a drug known to affect ribosomal proofreading increased the error rates due to mispairing at the wobble codon position. Starving bacteria for serine resulted in specific patterns of substitutions reflecting the amino acid deficiency. Overall, translation errors tend to occur at positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating that they are selected against. Genome wide ribosome density data suggest that errors occur at sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Together our results reveal a mechanistic basis for ribosome errors in translation.

systems biology↗