Biology subjects

Bolck, H. A.

Publications and source records attributed to Bolck, H. A..

5 recordsLinked to original sources

bioRxiv2025

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

bioRxiv2024

NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline

Omics profiling has proven of great use for unbiased and comprehensive identification of key features that define biological phenotypes and underlie medical conditions. While each omics profile assists characterization of specific molecular components relevant for the studied phenotype, their joint evaluation can offer deeper insights into the overall mechanistic functioning of biological systems. Here, we introduce an approach where starting from representative traits (e.g., differentially expressed elements) obtained for each omics profile, we construct and analyze joint interaction networks. The resulting networks rely on the existing knowledge of confident interactions among biological entities. We use these maps to identify and describe central elements, which connect multiple entities characteristic for the studied phenotypes and we leverage MONET network decomposition tool in order to highlight functionally connected network modules. In order to enable broad usage of this approach, we developed the NOODAI software platform, which enables integrative omics analysis through a user-friendly interface. The analysis outcomes are presented both as raw output tables as well as high-quality summary plots and written reports. Since the MONET tool enables the use of algorithms with strong performance in identifying disease-relevant modules, the NOODAI software platform can be of a high value for the analysis of clinical multi-omics datasets. Availability and ImplementationThe platform is available as a web application freely accessible at https://omics-oracle.com. The source code is freely available from GitHub under the GPL3 license at: https://github.com/TotuTiberiu/NOODAI.

bioinformatics↗

bioRxiv2024

Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids

Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternative splice isoforms. This complex phenomenon can be revealed by an in-depth understanding of cellular heterogeneity at the single-cell level. Recent advances in single-cell long- read sequencing technologies enable comprehensive transcriptome sequencing at the single-cell level. In this study, we have generated single-cell long-read sequencing of Patient-Derived Organoid (PDO) cells of clear-cell Renal Cell Carcinoma (ccRCC), an aggressive and lethal form of cancer that arises in kidney tubules. We have used the Multiplexed Arrays Sequencing (MAS-ISO-Seq) protocol of PacBio to sequence full-length transcripts exceptionally deep across 2,599 single cells to obtain the most comprehensive view of the alternative landscape of ccRCC to date. On average, we uncovered 86,182 transcripts across PDOs, of which 31,531 (36.6%) were previously uncharacterized. In contrast to known transcripts, many of these novel isoforms appear to exhibit cell-specific expression. Nonetheless, >50% of these novel transcripts were predicted to possess a complete protein-coding open reading frame. This finding suggests a biological role for these transcripts within kidney cells. Moreover, an analysis of the most dominant transcript switching events between ccRCC and non-ccRCC cells revealed that many switching events were cell and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our research elucidates the intricate transcriptomic architecture of ccRCC, potentially exposing the mechanisms underlying its aggressive phenotype and resistance to conventional cancer therapies.

cancer biology↗

bioRxiv2024

Comparison of Single-cell Long-read and Short-read Transcriptome Sequencing of Patient-derived Organoid Cells of ccRCC: Quality Evaluation of the MAS-ISO-seq Approach

Single-cell RNA sequencing is used in profiling gene expression differences between cells. Short-read sequencing platforms provide high throughput and high-quality information at the gene-level, but the technique is hindered by limited read length, failing in providing an understanding of the cell heterogeneity at the isoform level. This gap has recently been addressed by the long-read sequencing platforms that provide the opportunity to preserve full-length transcript information during sequencing. To objectively evaluate the information obtained from both methods, we sequenced four samples of patient-derived organoid cells of clear cell renal cell carcinoma and one healthy sample of kidney organoid cells on Illumina Novaseq 6000 and PacBio Sequel IIe. For both methods, for each sample, the cDNA was derived from the same 10x Genomics 3 single-cell gene expression cDNA library. Here we present the technical characteristics of both datasets and compare cell metrics and gene-level information. We show that the two methods largely overlap in the results but we also identify sources of variability which present a set of advantages and disadvantages to both methods.

genomics↗

bioRxiv2020

SF3B1 promotes tumor malignancy through splicing-independent co-activation of HIF1α

Mutations in the splicing factor SF3B1 are frequently occurring in various cancers and drive tumor progression through the activation of cryptic splice sites in multiple genes. Recent studies also demonstrate a positive correlation between expression levels of wildtype SF3B1 and tumor malignancy, but underlying mechanisms remain elusive. Here, we report that SF3B1 acts as an activator of HIF signaling through a splicing-independent mechanism. We demonstrate that SF3B1 forms a heterotrimer with HIF1 and HIF1{beta}, facilitating binding of the HIF1 complex to hypoxia response elements (HREs) to activate target gene expression. We further validate the relevance of this mechanism for tumor progression. Monoallelic deletion of Sf3b1 impedes formation and progression of hypoxic pancreatic cancer via impaired HIF signaling, but is well tolerated in normoxic chromophobe renal cell carcinoma. Our work uncovers an essential role of SF3B1 in HIF1 signaling, providing a causal link between high SF3B1 expression and aggressiveness of solid tumors.

cell biology↗