bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–08–25
fourteen papers selected by
Xiao Qin, University of Oxford
  1. Benchmarking computational methods for single-cell chromatin data analysis.
  2. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.
  3. Canalizing kernel for cell fate determination.
  4. Enhanced cellular longevity arising from environmental fluctuations.
  5. Small data methods in omics: the power of one.
  6. Entropy and adaptation: exploring cellular decision-making and cancer dynamics.
  7. Rationally reprogramming single-cell aging trajectories and lifespan through dynamic modulation of environmental inputs.
  8. How tumors hijack macrophages for immune evasion.
  9. Lack of basic rationale in epithelial-mesenchymal transition and its related concepts.
  10. Cells poised to divide can delay their commitment to proliferate.
  11. Novel immunotherapeutics against LGR5 to target multiple cancer types.
  12. Massively parallel analysis of single-molecule dynamics on next-generation sequencing chips.
  13. Cell fate simulation reveals cancer cell features in the tumor microenvironment.
  14. Hallmarks of regeneration.
  1. Genome Biol. 2024 Aug 16. 25(1): 225
    Benchmarking computational methods for single-cell chromatin data analysis.
    Siyuan Luo, Pierre-Luc Germain, Mark D Robinson, Ferdinand von Meyenn.
       BACKGROUND: Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices.
    RESULTS: We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection.
    CONCLUSIONS: Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.
    Keywords:  Benchmark; Clustering; Dimensional reduction; Feature engineering; ScATAC-seq
    DOI:  https://doi.org/10.1186/s13059-024-03356-x
  2. Nat Rev Mol Cell Biol. 2024 Aug 21.
    Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.
    Gunsagar S Gulati, Jeremy Philip D'Silva, Yunhe Liu, Linghua Wang, Aaron M Newman.
      Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
    DOI:  https://doi.org/10.1038/s41580-024-00768-2
  3. Brief Bioinform. 2024 Jul 25. pii: bbae406. [Epub ahead of print]25(5):
    Canalizing kernel for cell fate determination.
    Namhee Kim, Jonghoon Lee, Jongwan Kim, Yunseong Kim, Kwang-Hyun Cho.
      The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.
    Keywords:  Boolean network; canalizing kernel; cell fate change; master regulator; positive feedback loop
    DOI:  https://doi.org/10.1093/bib/bbae406
  4. Cell Syst. 2024 Aug 21. pii: S2405-4712(24)00206-0. [Epub ahead of print]15(8): 738-752.e5
    Enhanced cellular longevity arising from environmental fluctuations.
    Yuting Liu, Zhen Zhou, Hetian Su, Songlin Wu, Gavin Ni, Alex Zhang, Lev S Tsimring, Jeff Hasty, Nan Hao.
      Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.
    Keywords:  aging; caloric restriction; computational modeling; dynamical systems theory; longevity; metabolism; quantitative biology; single-cell imaging; systems biology; time-lapse microscopy
    DOI:  https://doi.org/10.1016/j.cels.2024.07.007
  5. Nat Methods. 2024 Aug 22.
    Small data methods in omics: the power of one.
    Kevin G Johnston, Steven F Grieco, Qing Nie, Fabian J Theis, Xiangmin Xu.
      Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.
    DOI:  https://doi.org/10.1038/s41592-024-02390-8
  6. J Physiol. 2024 Aug 18.
    Entropy and adaptation: exploring cellular decision-making and cancer dynamics.
    Mesut Tez.
      
    Keywords:  adaptation; cancer cells; cell biology
    DOI:  https://doi.org/10.1113/JP287385
  7. Cell Syst. 2024 Aug 21. pii: S2405-4712(24)00207-2. [Epub ahead of print]15(8): 676-678
    Rationally reprogramming single-cell aging trajectories and lifespan through dynamic modulation of environmental inputs.
    Matthew Smart, David F Moreno, Murat Acar.
      How do variations in nutrient levels influence cellular lifespan? A dynamical systems model of a core circuit involved in yeast aging suggests principles underlying lifespan extension observed at static and alternating glucose levels that are reminiscent of intermittent fasting regimens.
    DOI:  https://doi.org/10.1016/j.cels.2024.07.008
  8. Nat Cancer. 2024 Aug 16.
    How tumors hijack macrophages for immune evasion.
    Anders Hofer.
      
    DOI:  https://doi.org/10.1038/s43018-024-00808-y
  9. Cell Biosci. 2024 Aug 20. 14(1): 104
    Lack of basic rationale in epithelial-mesenchymal transition and its related concepts.
    Ying Cao.
      Epithelial-mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been considered as mainstream concepts and general rules driving developmental and pathological processes, particularly cancer. However, discrepancies and disputes over EMT and EMT research have also grown over time. EMT is defined as transition between two cellular states, but it is unanimously agreed by EMT researchers that (1) neither the epithelial and mesenchymal states nor their regulatory networks have been clearly defined, (2) no EMT markers or factors can represent universally epithelial and mesenchymal states, and thus (3) EMT cannot be assessed on the basis of one or a few EMT markers. In contrast to definition and proposed roles of EMT, loss of epithelial feature does not cause mesenchymal phenotype, and EMT does not contribute to embryonic mesenchyme and neural crest formation, the key developmental events from which the EMT concept was derived. EMT and MET, represented by change in cell shapes or adhesiveness, or symbolized by EMT factors, are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. Moreover, EMT and MET are consequences rather than driving factors of developmental and pathological processes. The true meaning of EMT in some developmental and pathological processes, such as fibrosis, needs re-evaluation. EMT is believed to endow malignant features, such as migration, stemness, etc., to cancer cells. However, the core property of cancer (tumorigenic) cells is neural stemness, and the core EMT factors are components of the regulatory networks of neural stemness. Thus, EMT in cancer progression is misattribution of the roles of neural stemness to the unknown mesenchymal state. Similarly, neural crest EMT is misattribution of intrinsic property of neural crest cells to the unknown mesenchymal state. Lack of basic rationale in EMT and related concepts urges re-evaluation of their significance as general rules for understanding developmental and pathological processes, and re-evaluation of their significance in scientific research.
    Keywords:  Basic rationale; Epithelial state; Epithelial–mesenchymal transition; Mesenchymal state; Neural stemness
    DOI:  https://doi.org/10.1186/s13578-024-01282-w
  10. Nature. 2024 Aug 21.
    Cells poised to divide can delay their commitment to proliferate.
      
    Keywords:  Cancer; Cell biology
    DOI:  https://doi.org/10.1038/d41586-024-02640-x
  11. EMBO Mol Med. 2024 Aug 21.
    Novel immunotherapeutics against LGR5 to target multiple cancer types.
    Hung-Chang Chen, Nico Mueller, Katherine Stott, Chrysa Kapeni, Eilidh Rivers, Carolin M Sauer, Flavio Beke, Stephen J Walsh, Nicola Ashman, Louise O'Brien, Amir Rafati Fard, Arman Godsinia, Changtai Li, Fadwa Joud, Olivier Giger, Inti Zlobec, Ioana Olan, Sarah J Aitken, Matthew Hoare, Richard Mair, Eva Serrao, James D Brenton, Alicia Garcia-Gimenez, Simon E Richardson, Brian Huntly, David R Spring, Mikkel-Ole Skjoedt, Karsten Skjødt, Marc de la Roche, Maike de la Roche.
      We have developed and validated a highly specific, versatile antibody to the extracellular domain of human LGR5 (α-LGR5). α-LGR5 detects LGR5 overexpression in >90% of colorectal cancer (CRC), hepatocellular carcinoma (HCC) and pre-B-ALL tumour cells and was used to generate an Antibody-Drug Conjugate (α-LGR5-ADC), Bispecific T-cell Engager (α-LGR5-BiTE) and Chimeric Antigen Receptor (α-LGR5-CAR). α-LGR5-ADC was the most effective modality for targeting LGR5+ cancer cells in vitro and demonstrated potent anti-tumour efficacy in a murine model of human NALM6 pre-B-ALL driving tumour attrition to less than 1% of control treatment. α-LGR5-BiTE treatment was less effective in the pre-B-ALL cancer model yet promoted a twofold reduction in tumour burden. α-LGR5-CAR-T cells also showed specific and potent LGR5+ cancer cell killing in vitro and effective tumour targeting with a fourfold decrease in pre-B-ALL tumour burden relative to controls. Taken together, we show that α-LGR5 can not only be used as a research tool and a biomarker but also provides a versatile building block for a highly effective immune therapeutic portfolio targeting a range of LGR5-expressing cancer cells.
    Keywords:  ADC; BiTE; CAR; Cancer Immunotherapeutics; LGR5
    DOI:  https://doi.org/10.1038/s44321-024-00121-2
  12. Science. 2024 Aug 23. 385(6711): 892-898
    Massively parallel analysis of single-molecule dynamics on next-generation sequencing chips.
    J Aguirre Rivera, G Mao, A Sabantsev, M Panfilov, Q Hou, M Lindell, C Chanez, F Ritort, M Jinek, S Deindl.
      Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe multiplexed single-molecule characterization at the library scale (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profiled the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding-rewinding dynamics. The ability to explore a large sequence space for Cas9 allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes.
    DOI:  https://doi.org/10.1126/science.adn5371
  13. J Biol Chem. 2024 Aug 20. pii: S0021-9258(24)02198-7. [Epub ahead of print] 107697
    Cell fate simulation reveals cancer cell features in the tumor microenvironment.
    Sachiko Sato, Ann Rancourt, Masahiko S Satoh.
      To elucidate the dynamic evolution of cancer cell characteristics within the tumor microenvironment (TME), we developed an integrative approach combining single-cell tracking, cell fate simulation, and three-dimensional (3D) TME modeling. We began our investigation by analyzing the spatiotemporal behavior of individual cancer cells in cultured pancreatic (MiaPaCa2) and cervical (HeLa) cancer cell lines, with a focus on the α2-6 sialic acid (α2-6Sia) modification on glycans, which is associated with cell stemness. Our findings revealed that MiaPaCa2 cells exhibited significantly higher levels of α2-6Sia modification, correlating with enhanced reproductive capabilities, whereas HeLa cells showed less prevalence of this modification. To accommodate the in vivo variability of α2-6Sia levels, we employed a cell fate simulation algorithm that digitally generates cell populations based on our observed data while varying the level of sialylation, thereby simulating cell growth patterns. Subsequently, we performed a 3D TME simulation with these deduced cell populations, considering the microenvironment that could impact cancer cell growth. Immune cell landscape information derived from 193 cervical and 172 pancreatic cancer cases was used to estimate the degree of the positive or negative impact. Our analysis suggests that the deduced cells generated based on the characteristics of MiaPaCa2 cells are less influenced by the immune cell landscape within the TME compared to those of HeLa cells, highlighting that the fate of cancer cells is shaped by both the surrounding immune landscape and the intrinsic characteristics of the cancer cells.
    Keywords:  3-dimensional tumor microenvironment simulation; Sambucus nigra lectin; Single-cell tracking; cancer cell heterogeneity; cancer cell lines; cell fate simulation; cervical cancer; live cell imaging; pancreatic cancer; stemness; tumor microenvironment; α2-6 sialic acid modification on glycans
    DOI:  https://doi.org/10.1016/j.jbc.2024.107697
  14. Cell Stem Cell. 2024 Aug 13. pii: S1934-5909(24)00257-1. [Epub ahead of print]
    Hallmarks of regeneration.
    Kenneth D Poss, Elly M Tanaka.
      Regeneration is a heroic biological process that restores tissue architecture and function in the face of day-to-day cell loss or the aftershock of injury. Capacities and mechanisms for regeneration can vary widely among species, organs, and injury contexts. Here, we describe "hallmarks" of regeneration found in diverse settings of the animal kingdom, including activation of a cell source, initiation of regenerative programs in the source, interplay with supporting cell types, and control of tissue size and function. We discuss these hallmarks with an eye toward major challenges and applications of regenerative biology.
    Keywords:  regeneration
    DOI:  https://doi.org/10.1016/j.stem.2024.07.007
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