Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease

The extent of microglial heterogeneity in humans remains a central yet poorly explored question in light of the development of therapies targeting this cell type. Here, we investigate the population structure of live microglia purified from human cerebral cortex samples obtained at autopsy and during neurosurgical procedures. Using single cell RNA sequencing, we find that some subsets are enriched for disease-related genes and RNA signatures. We confirm the presence of four of these microglial subpopulations histologically and illustrate the utility of our data by characterizing further microglial cluster 7, enriched for genes depleted in the cortex of individuals with Alzheimer’s disease (AD). Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data. Thus, our live human microglia identify a range of subtypes, and we prioritize one of these as being altered in AD.


Reporting Summary
Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, seeAuthors & Referees and theEditorial Policy Checklist .

Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. CellRanger software (from 10x Genomics) was used to align and quantify single-cell RNA-seq transcripts R statistical software was used to analyze data, and all custom code will be made available accompanying the publication Single-cell RNA-seq data is available through Synapse (synapse.org sublink is generated). All figures in the paper are based on this RNA-seq data.

nature research | reporting summary
October 2018

Life sciences study design
All studies must disclose on these points even when the disclosure is negative. Sample sizes were not calculated ahead of time, but we required at least 10 donors in total to assure that a sufficient degree of cell type heterogeneity would be investigated. As with most single-cell RNA-seq studies, the number of cells per donor is determined by the protocol, and is only roughly controllable.
Cells with fewer than 1000 transcripts (Unique Molecular Identifiers) were excluded from subsequent analyses. This threshold, a standard for low-depth sequencing, was chosen to ensure that downstream clustering and gene expression identification were not skewed by cells with poor detection rates.
In the manuscript we replicated the microglia clusters in an independent human single cell RNAs equencing dataset generated by our collaborators. We also replicated our findings in a published single nucleus RNA sequencing dataset.
The identification of clusters does not require the specification of sample groups, so no randomization was performed. Potential batch-related effects were accounted for in the clustering by regressing the count data against batch ID and clustering on the residuals, as described in the text.
Blinding was not relevant to the study, since the algorithm for identifying clusters does not take any sample or donor metadata into account. The anti Iba1 from Wako is an extensively used antibody to detect microglia in the brain. BioLegend's anti-human CD11b and CD45 antibodies are used by us since several years, and the transcriptomic analysis of cells sorted using these two antibodies confirmed that they label myeloid cells which in the brain is primarily microglia (PMID: 29416036). The specificity of the unconjugated anti-human CD45 antibody from Novus (CatNo: NB500-319, clone: MEM-28) has been validated on human peripheral blood (https://www.novusbio.com/products/cd45-antibody-mem-28_nb500-319). The Proteintech antibody (anti ISG15) was extensively validated by the vendor which efforts are documented on their website (https://www.ptglab.com/ products/ISG15-Antibody-15981-1-AP.htm#validation). The anti CD83 antibody was validated on human monocyte derived dendritic cells by the vendor (https://sandbox.biolegend.com/it-it/search-results/purified-anti-human-cd83-antibody-683). The anti CD74 antibody was validated on human peripheral blood lymphocytes (https://sandbox.biolegend.com/it-it/products/ purified-anti-human-cd74-antibody-4091). The anti human PCNA antibody was validated on human breast carcinoma tissue samples (https://www.thermofisher.com/antibody/product/PCNA-Antibody-clone-PC10-Monoclonal/13-3900).