. Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans. Cell Rep. 2020 Sep 29;32(13):108189. PubMed.


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  1. Thrupp et al. propose that snRNA-Seq technology fails to recapitulate microglia activation states in humans because activation genes are underrepresented in non-diseased human brains by snRNA-Seq. The paper reveals a difference in sensitivity between scRNA-Seq and snRNA-Seq. This is an important observation that highlights a limitation of snRNA-Seq. However, as the authors state in the discussion, they mainly looked at non-diseased brains. If upregulation of activation genes can be detected in Alzheimer’s disease versus control by snRNA-Seq, even though the gene abundance is lower compared to scRNA-Seq, then snRNA-Seq technology should still be able to identify differentially expressed genes. In fact, some of the genes identified in this study as depleted in nucleus, such as APOE and TREM2, were identified as upregulated in AD microglia by snRNA-Seq in Zhou et al., 2020, and Mathys et al., 2019

    The authors propose that failure to identify DAM counterparts in human AD in previous studies was due to intrinsic drawbacks of snRNA-Seq. In agreement with this, we do not exclude the possibility that technical limitations caused by snRNA-Seq lead to false-negative results. In this context, validation of the differences identified by snRNA-Seq at protein level, such as by IHC, becomes important. It is also wise to incorporate results from various methodologies, for example, spatial transcriptomics as the authors indicated, when making conclusions.

    To understand human diseases, it is important to identify changes between diseased and non-diseased tissues, hoping that these changes may provide new clues to disease mechanisms. Although scRNA-Seq may be more sensitive, it is not feasible in many cases, such as frozen autopsy specimens. Thus, although limited, snRNA-Seq remains a useful tool for detection of microglia activation.


    . Author Correction: Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease. Nat Med. 2020 Jun;26(6):981. PubMed.

    . Author Correction: Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019 Jul;571(7763):E1. PubMed.

    View all comments by Yingyue Zhou
  2. This study shows that a small population of genes implicated in microglial activation, such as APOE, CST3, SPP1, and CD74, is depleted in “nuclei” versus “whole cell” single-cell RNA sequencing technologies. Importantly, these genes are part of the disease-associated microglia (DAM) program discovered by the laboratory of Ido Amit.

    Although it is still unclear what the underlying reasons are for such differences, which the authors attribute to technical limitations inherent to snRNA-Seq, these findings underline that each technology on its own has clear advantages but also limitations. These are important to recognize, and openly discuss, in the community.

    This important study also stresses the need to use complementary technologies, as well as the crucial need to validate as much as possible any conclusions drawn from such high-dimensional approaches.

    View all comments by Florent Ginhoux
  3. Thrupp et al. compared single-nucleus (snRNA-Seq) and single-cell RNA-seq (scRNA-Seq) datasets generated from the temporal lobes of four human donors, and argue that the identification of microglial activation states—which are clearly implicated in AD pathogenesis—is impossible from single-nucleus data. The advent of high-throughput strategies for gene expression profiling of individual cells has opened up opportunities to characterize the cell states associated with Alzheimer’s disease. The use of single nuclei—which survive the freeze-thaw process intact—is potentially enormously enabling for these kinds of hypothesis-generating studies.

    This study used early technical protocols for performing snRNA-Seq on human tissue, and the pace of methodological improvement over the past several years has been swift. Experimental innovations, led by participants in the Brain Initiative Cell Census Network (BICCN) consortium, in the isolation of individual nuclei for snRNA-Seq, combined with the use of more modern commercially available kits, has increased the median gene detection in brain nuclei more than threefold (Yao et al., 2020). 

    Computational strategies for integrative analysis that flexibly model and correct for systematic biases across datasets and commercial platforms (Welch et al., 2019; Stuart et al., 2019; Fleming et al., 2019) have made it considerably easier to appreciate glial activation states. For example, in our recent snRNA-Seq analysis of seven postmortem samples, we were able to identify a distinct microglial state associated with cerebral amyloid angiopathy, marked by upregulation of genes such as APOE and CST3 (Welch et al., 2019). 

    We remain optimistic that, with these recent methodological improvements, the field is well-positioned to utilize these new technologies to characterize microglial states within human tissue samples. Given the rapid pace of progress in this exciting area, open sharing of new analytical tools, methods, and data will be critical for the field to wage a concerted effort to decipher the complexity of the cellular mechanisms driving Alzheimer’s disease.


    . Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity. Cell. 2019 Jun 13;177(7):1873-1887.e17. Epub 2019 Jun 6 PubMed.

    . Comprehensive Integration of Single-Cell Data. Cell. 2019 Jun 13;177(7):1888-1902.e21. Epub 2019 Jun 6 PubMed.

    View all comments by Evan Macosko
  4. Characterizing the transcriptomic changes of AD patient brains at the single-cell level enables the identification of cell-type-specific dysregulated pathways in AD. While single-nucleus RNA-sequencing (snRNA-Seq) allows examining the transcriptomic change in frozen human brain tissues, this recent study raised concerns on whether snRNA-Seq can provide representative transcriptome profile for microglia. Thrupp et al. suggested that snRNA-Seq is suboptimal to study microglial activation, as some of the microglial activation signature genes (i.e. APOE, SPP1) are depleted in nuclei. The authors found that the transcripts of 246 genes (~1.1 percent of detectable genes) are more abundantly detected using single-cell RNA-Seq (scRNA-Seq) when compared to that of snRNA-Seq. Interestingly, many of these genes are the signature genes of disease-associated microglia, a specific subpopulation of microglia that associated with neurodegeneration. Thus, the authors raised a concern on the use of snRNA-Seq in detecting microglia activation in AD (Grubman et al., 2019; Mathys et al., 2019; Zhou et al., 2020). 

    While snRNA-Seq could potentially reduce the sensitivity of examining the microglial state transition, our recent study on the snRNA-Seq analysis of AD patient brains was able to show microglial activation in AD (Lau et al., 2020). Specifically, we found that microglia have increased expression of APOE, HLA-DQB1 and PTPRG in AD, in line with previous AD snRNA-Seq studies (Mathys et al., 2019). Although the transcript level of disease-associated microglia signature genes decreases in snRNA-Seq, these genes remain within the top 500 abundant genes in microglial nuclei. Thus, the transcriptome profiling by snRNA-Seq may be sufficient to illustrate the transition of microglial state in AD condition.

    Indeed, our unpublished snRNA-Seq data can also demonstrate the microglial state transition in response to IL-33 (i.e., the induction of IL-33-responsive microglia) in an Aβ deposition mouse model, similar to what we have observed in our recent scRNA-Seq study (Lau et al., 2020). This shows that while snRNA-Seq is unable to capture the complete transcriptome signature of distinct activated microglia, we can still characterize the activation state of microglia by some representative signature genes in human brains. With further optimization of snRNA-Seq method and analysis, we are optimistic that we can study microglial state transition in AD using snRNA-Seq.


    . A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019 Dec;22(12):2087-2097. PubMed.

    . Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer's disease. Proc Natl Acad Sci U S A. 2020 Oct 13;117(41):25800-25809. Epub 2020 Sep 28 PubMed.

    . IL-33-PU.1 Transcriptome Reprogramming Drives Functional State Transition and Clearance Activity of Microglia in Alzheimer's Disease. Cell Rep. 2020 Apr 21;31(3):107530. PubMed.

    . Author Correction: Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019 Jul;571(7763):E1. PubMed.

    . Author Correction: Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer's disease. Nat Med. 2020 Jun;26(6):981. PubMed.

    View all comments by Nancy Ip

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  1. Single-nucleus RNA Sequencing Misses Activation of Human Microglia