• scRNA-seq of BM aspirates with enrichment of stromal cells and prediction of cell-cell interactions
• Performed staining of FFPE human BM slides to locate cell types

Paper that Florian mentioned

• Really cool paper from Haas/Trumpp/Velten labs
• Precise definitions of hematopoietic cells in the bone marrow based on surface marker expression and 462 mRNAs
• Useful for people who want to isolate cell types with FACS but also lots of good marker genes for using with scRNA-seq
• Found that the surface protein is only modestly affected by ageing but the majority of AML cells clustered away from the healthy differentiation trajectory > "Our data resource and bioinformatic advances enable the efficient identification and isolation of any molecularly defined cell state from blood and bone marrow while laying the grounds for reconciling flow cytometry and single-cell genomics data across human tissues."

• Really cool gifs of droplet-based single-cell sequencing

• Scanpy-style clustered dotplot tutorial in R

• Nice summary of the case for using ontologies to define cell types
• Highlights
  • "Before the age of single-cell genomics, a rigorous definition was usually not necessary. Colloquially, a cell type is a category of cells in the body that performs a certain function."
  • "such a fuzzy definition does not suffice as a foundational definition from which one could go on to create 'reference maps'"
  • "as researchers measure more cells, they tend to find more 'cell types'"
  • "First, let’s get the obvious out of the way: the concept of “cell type” is human-made. Nature does not create categories, rather, we create categories in our minds."
  • "I argue that one can define a cell type to simply be a subset of cell states in the cellular state space. For example, when one talks about a “T cell”, they are inherently talking about all states in the cell state space in which the cell is performing a function that we have named “T cell”."
  • "The idea of defining cell types to be subsets of cell states enables one to define disease cell types... Because diseased cell types are represented in the same framework as any other cell type, we can add them to an ontology of cell types"
  • "multiple batches [...] results in two disconnected, and approximately isomorphic subgraphs."
  • "a useful mental model for thinking about cell types, cell states, and for placing open problems in bioinformatics into a common conceptual framework"

• The task of differential expression in essence boils down to creating a model of the form expression=b⋅[is diseased]+a and investigating whether the [is diseased] label helps predicting the gene expression level.
• It is important that the between-individual variation is not larger than the between-conditions variation.
• This means that when accounting for individual-specific effects on gene expression, it will never be possible to get low p-values.
• Multi-level regression: Say there is an overall mean expression level, and a between-mouse variance. Then for each mouse there is a mean expression level, with between-cell variance for that particular mouse.