Single-cell multi-omics and lineage tracing to dissect cell fate decision-making

Haghverdi, Laleh; Ludwig, Leif S.

doi:10.1016/j.stemcr.2022.12.003

Cited by 14 publications

(8 citation statements)

References 103 publications

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“…The development of cutting-edge imaging and omics technologies stands as the panacea to surmount this challenge. For instance, single-cell multi-omics approaches have the potential to unveil granular insights into individual cell behaviors over time [ 165 , 267 – 269 ]. Advanced imaging techniques, including super-resolution microscopy and live cell imaging, offer high-resolution spatial and temporal data [ 270 – 274 ].…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Chu,

Wang,

et al. 2024

Military Med Res

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Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

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Section: Challenges and Future Prospectsmentioning

confidence: 99%

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Chu,

Wang,

et al. 2024

Military Med Res

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“…Other single cell omics (ATACseq, ChIPseq, etc.) and multiomics technologies (to obtain simultaneous RNA expression and epigenetic information from the same individual cell) will provide further insight into cell state and cell fate decision making [ 45 , 46 ].…”

Section: Five Breakthroughs Of the Past Decadementioning

confidence: 99%

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

Amerongen

Bentires‐Alj

Boxtel

et al. 2023

J Mammary Gland Biol Neoplasia

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On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

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“…Recent strides in single-cell omics sequencing technologies have ushered in a new era of cellular exploration, offering insights into developmental stages, cellular phenotypes, and pathogenesis ( Haghverdi & Ludwig, 2023 ; Li & Wang, 2021 ; Nomura, 2021 ). By delving into the profiles of various modalities such as transcriptome and epigenome, single-cell studies empower precise profiling at the individual cell level.…”

Section: Introductionmentioning

confidence: 99%

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Choi,

Park,

Chae

2024

PeerJ

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets—gene expression, DNA methylation, and DNA accessibility—while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer’s superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

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Single-cell multi-omics and lineage tracing to dissect cell fate decision-making

Cited by 14 publications

References 103 publications

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

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