Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

Chen, Kevin; Öztürk, Kıvılcım; Contreras, Ryne L.; Simon, Jessica; McCann, Sean; Chen, Wei Ji; Carter, Hannah; Fraley, Stephanie I.

doi:10.1016/j.isci.2020.101991

Cited by 12 publications

(14 citation statements)

References 24 publications

(29 reference statements)

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“…Cellular energetics are linked to migration speed ( 7 ) and the metabolic reprogramming of cancer cells impacts their metastatic potential ( 8 , 9 ), suggesting metabolism impacts migration. Cancer cells phenotypically sorted based on invasion do demonstrate unique molecular programs including those related to metabolism ( 10 ). Here, we examined the metabolism of the cell population and bioenergetics of individual cells encapsulated in a wide range of collagen densities to manipulate migration difficulty.…”

mentioning

confidence: 99%

Highly motile cells are metabolically responsive to collagen density

Zanotelli

Zhang

Ortiz

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Altered tissue mechanics and metabolism have gained significant attention as drivers of tumorigenesis, and mechanoresponsive metabolism has been implicated in migration and metastasis. However, heterogeneity in cell populations makes it difficult to link changes in behavior with metabolism, as individual cell behaviors are not necessarily reflected in population-based measurements. As such, the impact of increased collagen deposition, a tumor-associated collagen signature, on metabolism remains ambiguous. Here, we utilize a wide range of collagen densities to alter migration ability and study the bioenergetics of individual cells over time. Sorting cells based on their level of motility revealed energetics are a function of collagen density only for highly motile cells, not the entire population or cells with low motility. Changes in migration with increasing collagen density were correlated with cellular energetics, where matrix conditions most permissive to migration required less energy usage during movement and migrated more efficiently. These findings reveal a link between matrix mechanics, migratory phenotype, and bioenergetics and suggest that energetic costs are determined by the extracellular matrix and influence cell motility.

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confidence: 99%

Highly motile cells are metabolically responsive to collagen density

Zanotelli

Zhang

Ortiz

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…To further test this hypothesis, we compared the cell population dynamics in cultured cells and the orthotopic primary tumors by analyzing publicly available scRNA-seq datasets. By integrating scRNA-seq data from 3D cultured 4T1 cells (GSM4812003) (46) and tumor cells isolated from orthotopically fat pad-injected 4T1 primary tumor (PT) (GSM3502134) (47) (Figure 4A, B), we observed 5 clusters. Clusters 0, 1, 2, 4 were predominant in cultured tumor cells, while cluster 3 was predominant in primary tumors.…”

Section: Resultsmentioning

confidence: 99%

Tumor-educated Gr1⁺CD11b⁺cells instigate breast cancer metastasis by twisting cancer cells plasticity via OSM/IL6–JAK signaling

Peyvandi¹,

Bulliard²,

Kauzlaric³

et al. 2022

Preprint

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Cancer cell plasticity contributes to tumor therapy resistance and metastasis formation, which represent the main causes of cancer-related death for most cancers, including breast cancer. The tumor microenvironment drives cancer cell plasticity and metastasis and unravelling the underlying cues may provide novel effective strategies to manage metastatic disease. Here we show that stem cell antigen-1 positive (Sca-1+) murine breast cancer cells enriched during tumor progression and metastasis have higherin vitrocancer stem cell-like properties, enhancedin vivometastatic ability, and initiate primary tumors rich in Gr1highCD11b+Ly6Clowcells. In turn, tumor-educated Gr1+CD11b+(Tu-Gr1+CD11b+) cells rapidly and transiently convert low metastatic 4T1-Sca-1-cells into highly metastatic 4T1-Sca-1+cells via secreted OSM and IL6. Moreover, chemotherapy-resistant and highly metastatic 4T1-derived cells maintain high Sca-1+frequency through cell autonomous IL6 production. Inhibition of OSM, IL6 or JAK suppressed Tu-Gr1+CD11b+-induced Sca-1+population enrichmentin vitro, while JAK inhibition abrogated metastasis of chemotherapy-enriched Sca-1+cellsin vivo. Importantly, Tu-Gr1+CD11b+cells invoked a gene signature in tumor cells predicting shorter OS and RFS in breast cancer patients. Collectively, our data identified OSM/IL6-JAK as a clinically relevant paracrine/autocrine axis instigating breast cancer cell plasticity triggering metastasis.

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“…belong to this type of method. This method proved to be valuable in identifying new cell populations [ 11 , 12 , 13 , 14 , 15 , 16 ]. However, this annotation step is tedious and time-consuming, because it involves manual examination of cluster-specific marker genes and requires a priori knowledge of known cellular markers.…”

Section: Introductionmentioning

confidence: 99%

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

et al. 2023

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Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability.

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Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

Cited by 12 publications

References 24 publications

Highly motile cells are metabolically responsive to collagen density

Highly motile cells are metabolically responsive to collagen density

Tumor-educated Gr1⁺CD11b⁺cells instigate breast cancer metastasis by twisting cancer cells plasticity via OSM/IL6–JAK signaling

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

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Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

Cited by 12 publications

References 24 publications

Highly motile cells are metabolically responsive to collagen density

Highly motile cells are metabolically responsive to collagen density

Tumor-educated Gr1+CD11b+cells instigate breast cancer metastasis by twisting cancer cells plasticity via OSM/IL6–JAK signaling

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

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Tumor-educated Gr1⁺CD11b⁺cells instigate breast cancer metastasis by twisting cancer cells plasticity via OSM/IL6–JAK signaling