Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Ianevski, Aleksandr; Giri, Anil; Aittokallio, Tero

doi:10.1101/812131

Cited by 7 publications

(7 citation statements)

References 61 publications

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“…Once trained, it can be used for supervised classification of future datasets from similar tissues/samples. ScType (Ianevski et al, 2019), another software package for automated cell cluster annotation, is primarily data driven and independent of reference datasets. Its performance relies on prioritizing marker genes (known or de novo) among top upregulated DEGs and guaranteeing their specificity across cell clusters and cell types at once in an scRNA-seq experiment in a totally unsupervised manner.…”

Section: Marker Gene Identification and Cell Cluster Annotationmentioning

confidence: 99%

“…It also allows identification of novel marker genes with high specificity for either known or new cell types. However, since the majority of these tools have not been widely adopted by other bioinformaticians, their individual or combined accuracy remains to be tested against more high-quality scRNA-seq datasets and improved according to the benchmarking findings (Abdelaal et al, 2019;Ianevski et al, 2019;Zhao et al, 2019). Recently, rapid progress has been made in the development of spatial transcriptomics techniques that can be correlated and complemented with single-cell transcriptomics (Sta ˚hl et al, 2016;Rodriques et al, 2019;Vickovic et al, 2019;Stickels et al, 2020).…”

Section: Marker Gene Identification and Cell Cluster Annotationmentioning

confidence: 99%

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Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

2021

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Section: Marker Gene Identification and Cell Cluster Annotationmentioning

confidence: 99%

Section: Marker Gene Identification and Cell Cluster Annotationmentioning

confidence: 99%

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

2021

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“…Cell-specific marker information was automatically extracted from our ScType marker database 31 , and unassigned cell types were manually identified based on the specific expression markers:…”

Section: Cell Type Identification and Manual Annotationmentioning

confidence: 99%

Patient-tailored design of AML cell subpopulation-selective drug combinations

Ianevski

Lahtela

Javarappa

et al. 2020

Preprint

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The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

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“…The enormous number of potential con!gurations of ligand designs make computational tools essential for designing highly selective therapies. 11 Here, we analyze a suite of molecular approaches for engineering cell-speci!c binding using a multi-valent, multi-receptor, multi-ligand binding model. We show that strategies including a"nity, valency, binding competition, ligand mixtures, and heterovalent complexes provide distinct improvements in cell-speci!c targeting.…”

Section: Introductionmentioning

confidence: 99%

A quantitative view of strategies to engineer cell-selective ligand binding

Tan

Orcutt-Jahns

Meyer

2021

Integrative Biology

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A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.

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Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Cited by 7 publications

References 61 publications

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

Patient-tailored design of AML cell subpopulation-selective drug combinations

A quantitative view of strategies to engineer cell-selective ligand binding

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