Guidelines for the use of flow cytometry and cell sorting in immunological studies.
https://www.ncbi.nlm.nih.gov/pubmed/29023707/
https://www.ncbi.nlm.nih.gov/pubmed/29023707/
Good information about shRNA design and screen:
The RNAi Consortium (TRC)
https://portals.broadinstitute.org/gpp/public/
https://www.nature.com/nmeth/journal/v3/n9/full/nmeth924.html
The RNA-guided CRISPR-Cas9 nuclease from Streptococcus pyogenes (SpCas9) has been widely repurposed for genome editing1-4. High-fidelity (SpCas9-HF1) and enhanced specificity (eSpCas9(1.1)) variants exhibit substantially reduced off-target cleavage in human cells, but the mechanism of target discrimination and the potential to further improve fidelity are unknown5-9. Using single-molecule Förster resonance energy transfer (smFRET) experiments, we show that both SpCas9-HF1 and eSpCas9(1.1) are trapped in an inactive state10 when bound to mismatched targets. We find that a non-catalytic domain within Cas9, REC3, recognizes target complementarity and governs the HNH nuclease to regulate overall catalytic competence. Exploiting this observation, we designed a new hyper-accurate Cas9 variant (HypaCas9) that demonstrates high genome-wide specificity without compromising on-target activity in human cells. These results offer a more comprehensive model to rationalize and modify the balance between target recognition and nuclease activation for precision genome editing.
Cancer cells exploit the expression of the programmed death-1 (PD-1) ligand 1 (PD-L1) to subvert T-cell-mediated immunosurveillance. The success of therapies that disrupt PD-L1-mediated tumour tolerance has highlighted the need to understand the molecular regulation of PD-L1 expression. Here we identify the uncharacterized protein CMTM6 as a critical regulator of PD-L1 in a broad range of cancer cells, by using a genome-wide CRISPR-Cas9 screen. CMTM6 is a ubiquitously expressed protein that binds PD-L1 and maintains its cell surface expression. CMTM6 is not required for PD-L1 maturation but co-localizes with PD-L1 at the plasma membrane and in recycling endosomes, where it prevents PD-L1 from being targeted for lysosome-mediated degradation. Using a quantitative approach to profile the entire plasma membrane proteome, we find that CMTM6 displays specificity for PD-L1. Notably, CMTM6 depletion decreases PD-L1 without compromising cell surface expression of MHC class I. CMTM6 depletion, via the reduction of PD-L1, significantly alleviates the suppression of tumour-specific T cell activity in vitro and in vivo. These findings provide insights into the biology of PD-L1 regulation, identify a previously unrecognized master regulator of this critical immune checkpoint and highlight a potential therapeutic target to overcome immune evasion by tumour cells.
Similar data was also published back to back by the Schumacher lab
We’re very happy about recently received funding from the “Clas Groschinsky minnesfond” –> http://www.groschinsky.org/
A variety of tissue lineages can be differentiated from pluripotent stem cells by mimicking embryonic development through stepwise exposure to morphogens, or by conversion of one differentiated cell type into another by enforced expression of master transcription factors. Here, to yield functional human haematopoietic stem cells, we perform morphogen-directed differentiation of human pluripotent stem cells into haemogenic endothelium followed by screening of 26 candidate haematopoietic stem-cell-specifying transcription factors for their capacity to promote multi-lineage haematopoietic engraftment in mouse hosts. We recover seven transcription factors (ERG, HOXA5, HOXA9, HOXA10, LCOR, RUNX1 and SPI1) that are sufficient to convert haemogenic endothelium into haematopoietic stem and progenitor cells that engraft myeloid, B and T cells in primary and secondary mouse recipients. Our combined approach of morphogen-driven differentiation and transcription-factor-mediated cell fate conversion produces haematopoietic stem and progenitor cells from pluripotent stem cells and holds promise for modelling haematopoietic disease in humanized mice and for therapeutic strategies in genetic blood disorders.
Author information:
1 Stem Cell Transplantation Program, Division of Pediatric Hematology and Oncology, Dana-Farber Cancer Institute, Boston Children’s Hospital and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
2 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA.
3 Harvard Stem Cell Institute, Cambridge, Massachusetts 02138, USA.
4 Manton Center for Orphan Disease Research, Boston, Massachusetts 02115, USA.
5 Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, Boston Children’s Hospital, Boston, Massachusetts, USA.
6 Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
7 Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, Massachusetts, 02215, USA.
8 Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.
9 Department of Biology, Brandeis University, Waltham, Massachusetts 02453, USA.
10 Program in Computer Science, Harvard University, Cambridge, Massachusetts, USA.
11 McEwen Centre for Regenerative Medicine, University Health Network, Toronto, Ontario M5G 1L7, Canada.
12 Division of Gastroenterology, Brigham and Women’s Hospital, Boston, Massachusetts, USA.
13 Howard Hughes Medical Institute, Boston, Massachusetts 02115, USA.
Link to software –> www.intomics.com/inbio/map/#home
Based on this publication:
A scored human protein-protein interaction network to catalyze genomic interpretation.
Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz G, Workman CT, Rigina O, Rapacki K, Stærfeldt HH, Brunak S, Jensen TS, Lage K.
Nat Methods. 2017 Jan;14(1):61-64. doi: 10.1038/nmeth.4083. Epub 2016 Nov 28.
Link to publication –> www.ncbi.nlm.nih.gov/pubmed/27892958
“ACCENSE is a tool for exploratory analysis of high-dimensional single-cell data such as that generated by Mass Cytometry (CyTOF™, Fluidigm Corp.). By combining a nonlinear dimensionality reduction algorithm (t-SNE or Barnes-Hut SNE) with a k-means clustering algorithm both visualization for exploratory analysis and automated cell classification into subpopulations is performed.”
