cell rna sequence profiling  (Addgene inc)

Journal: Cell reports

Article Title: Calcium-permeable AMPA receptors on AII amacrine cells mediate sustained signaling in the On-pathway of the primate retina

doi: 10.1016/j.celrep.2022.111484

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: Visualizations of single-cell RNA-sequencing expression profiles from peripheral primate retinal ganglion cells and amacrine cells were generated from a published dataset ( ) using the Single Cell Portal (Broad Institute).

Techniques: Recombinant, Software

Journal: bioRxiv

Article Title: The OncoLoop Network-based Precision Cancer Medicine Framework

doi: 10.1101/2022.02.11.479456

Figure Lengend Snippet: A. Conceptual overview : OncoLoop was designed to identify high-fidelity (cognate) models of a patient tumor— i.e. , GEMM-derived tumors (GEMM-DTs), in this study—as well as drugs capable of inverting the activity of MR proteins identified from both the patient and his cognate GEMM-DT samples for follow-up, patient-relevant validation. To accomplish this goal, OncoLoop performs integrative analysis of transcriptomic (RNA-seq) profiles from a patient tumor, his cognate model, and large-scale drug perturbation assays. B. Regulatory network analysis : Gene expression profiles generated from each data source were used to reverse-engineer species- and cohort-specific regulatory networks, which are then used to transform differential gene expression signature from individual samples into differential protein activity profiles. Drug perturbation profiles were analyzed with patient-derived networks (See Methods). C. OncoLoop analysis : Gene Set Enrichment Analysis, as implemented by the aREA algorithm , was used to assess the overlap in differentially active MR proteins between a human tumor and its cognate GEMM-DTs ( OncoMatch algorithm ). Similarly, aREA-based enrichment analysis was used to identify drugs capable of inverting the activity of a tumor’s MRs in drug- vs. vehicle control treated cells ( MR-inverter drugs ), for each patient and cognate GEMM-DT(s) pairs. D. Drug prediction and validation: Representative Circos plot illustrating PGD-loops generated by matching a patient (P) to a GEMM-DT (G) by identifying the same MR-inverter drug (D) for both. Candidate drugs were first prioritized by pharmacotype analysis (i.e., subset of patients/GEMM-DTs predicted to be sensitive to the same subset of drugs) and then validated in vivo using, both in cognate GEMM-DT-derived allografts as well as in MR-matched PDX models.

Article Snippet: Cell Line RNA-seq profiles were downloaded from the Broad Institute web portal ( https://data.broadinstitute.org/ccle/ ) from the Cancer Cell Line Encyclopedia (CCLE) as transcript per million (TPM) measurements with timestamp version 20180929.

Techniques: Derivative Assay, Activity Assay, Biomarker Discovery, RNA Sequencing, Gene Expression, Generated, Control, In Vivo