Studying Alzheimer’s at single cell resolution

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Cells vary considerably within cell populations, including within a particular type of tissue or cell. No two cells have the same response to their surroundings, since each cell’s behavior is dictated by the particular genes it expresses and at what level. This unique gene expression is what controls how the cell performs in the body.

Traditional gene expression analysis involved profiling whole cell populations and averaging measurements across those populations. Today, however, it is possible to study cells at single-cell resolution, which has opened up new possibilities in terms of understanding cells on an individual level.


Single-cell analysis in neurological disease

Even within a single brain region, there is significant variation between the morphology, connectivity and electrophysical properties of individual neurons. A key step towards understanding the basic components of the nervous system is systematic classification of individual neurons. For cells to be classified on a molecular basis, gene expression must be assessed at single-cell resolution.

Neurological diseases such as Alzheimer’s are often too complex for researchers to be able to develop effective treatments, due to the heterogeneity of the neurons underlying the disease. Using single-cell tool kits, researchers can study heterogeneity within cell populations, single out rare cells, study interactions between diverse cell types and improve their understanding of how such interactions are relevant to health and disease states.

Cell isolation and capture

Several techniques are available for isolating and capturing cells for single-cell analysis, including manual or automated micropipetting, laser capture microdissection, fluorescence-activated cell sorting (FACS) and microdroplet devices. Another example is microfluidic platforms, which not only enable single-cell capture, but also automation of certain downstream biochemical reactions. Future development of microfluidics technology will lead to ever greater increases in the throughput of microfluidic cell capture and the isolation of single cells.


One type of microfluidics technology, namely microengraving, enables multiple secreted analytes to be quantified, by culturing cells in a dense array of nanowells.

In a study by Tracy Young-Pearse and colleagues (2016), the team adapted microengraving to create a new technology that can, for the first time, detect secreted analytes that are relevant to Alzheimer’s disease. Secreted factors are known to play an important role in both healthy and pathological processes, across all types of body tissue. Using their new technique, the researchers were able to uncover the dynamic range of secretion profiles of the analytes from single, living human neurons and astrocytes. They identified subpopulations of these cells that secrete the analytes in high concentrations and then molecularly characterized them using immunostaining and RNA sequencing (RNA-seq).

RNA-seq development

RNA-seq is a recently developed high-throughput technique for gene expression analysis. Although RNA-seq has clear advantages over previous techniques, capturing rare dynamic processes, such as adult neurogenesis, can be challenging, since isolating rare neurons is difficult and there are limited markers for each phase.

In a paper by Naomi Habib and Yinqing Li (2016), the researchers report on their new method for studying rare cell types in the brain. The technique combines the sequencing of RNA from isolated nuclei (sNuc-Seq) with tagging of regenerating cells (Div-Seq). Habib and Li stress that targeted and effective therapies can only be developed once it is possible to achieve a full atlas of every type of neuron at single-cell resolution and establish exactly which cells are causing the disease.


  • Skene N and Grant S. Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment. Front. Neurosci. 2016;10:16. DOI: 10.3389/fnins.2016.00016
  • Young-Pearse T, et al. Single-Cell Detection of Secreted Aβ and sAPPα from Human IPSC-Derived Neurons and Astrocytes. Journal of Neuroscience 2016;36 (5); 1730–1746
  • Awatramani R, et al. Disentangling neural cell diversity using single-cell transcriptomics. Nature Neuroscience 2016;19:1131–1141
  • Kimmerling, R. J. et al. A microfluidic platform enabling single cell RNA-seq of multigenerational lineages. Nat. Commun 2016. 7:10220 DOI: 10.1038/ncomms10220
  • Snyder M, et al. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 2009;10:57–63
  • Habib N, et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 2016: DOI: 10.1126/science.aad7038
  • Meade-Kelly V. Broadminded Blog. Divide and conquer: New single cell approach broadens range of cell types that can be studied in the brain. Broad Institute 2016. Available at:

Source – News-

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