27 Aug 2024

In the brain, location is everything. And the throngs of cells that live there are nothing without the billions of distinct connections between them. Although scientists can use single-cell transcriptomics to survey gene expression in individual cells throughout the brain, these methods don’t capture the spatial context of those cellular states. Spatial omics has arisen to home in on that. The AAIC held last month in Philadelphia featured the newest findings from the leading edge of this rapidly advancing field, which combines genomics, neuropathology, and high-resolution imaging to give various omes a physical address in the brain.

Drawing from a burgeoning toolbox of techniques, researchers investigated how Aβ plaques and tau tangles influence both composition and activity of cells stationed nearby. They tracked how cells responded as amyloid plaques formed and matured, and identified inflammatory flare-ups that could fuel amyloid-induced tau pathology. The activities of plaque-adjacent glial cells may explain how some people manage to fend off tau tangles and cognitive decline, despite harboring amyloid plaques.

“There’s nowhere in the body where the exact location of a cell is more important than it is in the brain,” said Bart de Strooper of KU Leuven in Belgium who, along with Lea Grinberg of the University of California, San Francisco, co-chaired a session devoted to emerging spatial omics findings at the meeting.

An early adopter of spatial transcriptomics, De Strooper’s lab had previously identified plaque-induced genes, aka PIGs, expressed by cells positioned near Aβ aggregates (Jul 2020 news). Since then, scientists there and elsewhere have sharpened the techniques’ spatial resolution and scope, and they worked out how to simultaneously map transcriptomes and pathological proteins in the same tissue (Mallach et al., 2024; Aug 2022 conference news; Feb 2023 news).

Researchers at the Allen Institute for Brain Science in Seattle continue to improve these techniques, with the goal of mapping the mouse and, ultimately, the human brain, in stunning detail (Dec 2023 news). In Philadelphia, Allen’s Rebecca Hodge noted that while spatial omics technologies seem complex, they come in two main flavors.

Imaging-based approaches, such as MERFISH and Xenium, use nucleic acid probes that bind to specific RNA sequences within a tissue section. Using sequential imaging to quantify each target’s abundance, scientists can take stock of hundreds to thousands of transcripts at single-cell, even subcellular, resolution. This technique is limited by the number of transcripts it can detect.

On the other hand, sequencing-based techniques, such as Visium or Slide-Seq can survey entire transcriptomes but are limited in their cellular resolution. In these approaches, transcripts within tissue sections are transferred onto chips or slides that are dotted with barcoded spots that impart spatial information. Once the transferred transcripts are tagged with their location, they can be sequenced, yielding both spatial and transcriptomic information.

At AAIC, Hodge presented initial results from the Seattle Alzheimer’s Disease Cell Atlas. A partnership among A partnership among the Allen Institute, the University of Washington in Seattle, and the Kaiser Permanente Washington Health Research Institute, also in Seattle, SEA-AD aims to understand SEA-AD aims to understand how, and where, cells respond at different disease stages. To do this, scientists use neuropathology, single-cell transcriptomics, and spatial transcriptomics to analyze brain samples from people who died at different stages across the AD neuropathological spectrum. Findings are posted on bioRxiv (Gabitto et al., 2024). 

A few highlights? For one, the researchers hunted for subsets of neurons that are vulnerable to AD pathology. With snRNA sequencing, they pinpointed transcriptional subtypes of cells in the middle temporal gyrus and the dorsolateral prefrontal cortex, which are affected by tau pathology early and late in AD, respectively. This identified 139 transcriptional subtypes of cells, including several that became scarcer as the pathological burden worsened. These included subsets of excitatory neurons as well as somatostatin-expressing interneurons. Because excitatory neurons express specific markers in different layers of the neocortex, the researchers inferred that the vulnerable subsets of these neurons resided in layers 2 and 3. However, because interneurons are distributed diffusely across different cortical layers, their locations are not closely tied to their transcriptomes.

Enter spatial transcriptomics. Using MERFISH to map the expression of 140 transcripts with single-cell resolution across 76 sections of the MTG from 28 brains, the researchers spotted the most vulnerable subtypes of neurons dwelled in the outermost layers of the neocortex. They included for somatostatin-positive interneurons, parvalbumin-expressing inhibitory neurons, and excitatory neurons. The lone exception was a subtype of excitatory neuron found deep within layer 5, which became vulnerable only in late stages of AD, i.e., among brain samples with tau tangles inundating the prefrontal cortex.

To Hodge, the findings exemplify that understanding where these cell types are within the context of intact tissue is important. “Now we can start to generate hypotheses about how the loss of these different neurons within the same layer, where they’re likely interacting, might impact the circuitry and function of the brain,” she said.

However, she added that this analysis lacks a critical piece of spatial information, namely, how being near pathological proteins influences a given cell’s gene expression. To address this question, Hodge and colleagues are adding immunostaining of Aβ and p-tau to spatial transcriptomics (image below).

Pathological Neighborhood. Different subtypes of neuron (A) and microglia (B) were mapped across neocortical layers with spatial transcriptomics, then Aβ and p-tau were stained (C). Overlaying this spatial data (D) could address how being near to pathology influences the states of nearby cells. [Courtesy of Rebecca Hodge, Allen Institute for Brain Science.]

Many challenges lie ahead for spatial transcriptomics, Hodge said. Chief among them: the sheer scale of the human brain. “Its size exceeds the imaging or capture size of every product on the market,” she said. To chip away at it, Allen Institute scientists are tiling across large anatomical structures to create series of tissue blocks that fit within their imaging machines. This technique is possible yet technically demanding, Hodge said, as it requires tracking many blocks followed by stitching images back together to recreate the anatomy of the brain.

Other scientists are approaching the problem by clearing sections of the brain with hydrogels. This allows them to image large, three-dimensional chunks. A recent study led by Kwanghun Chung at Massachusetts Institute of Technology used this method to visualize different morphological flavors of microglia congregating around Aβ plaques (Park et al., 2024). “It will be exciting to see how this technique might be used to look at both RNA and proteins in these large-scale tissues,” Hodge said.

De Strooper noted the pace of technological advancement in this field. “It’s fascinating to see how fast these things move,” he said after Hodge’s talk.

Yanling Wang of Rush University in Chicago investigates how Aβ plaques influence nearby neurons, and how glial cells recruited to the scene shake things up. In Philadelphia, Wang presented findings from her analysis of 21 postmortem brain samples from the ROS-MAP cohort, in which the researchers used Visium to map transcriptomes within sections of prefrontal cortex. Each finely sliced section was placed on a Visium slide dotted with nearly 260,000 spots, each big enough to cover the area of five to 10 cells. Once embedded with their spatial information, the transcripts were sequenced. For each section scrutinized in this way, two adjacent slices were subjected to immunohistochemistry to label Aβ, GFAP, and Iba1 to delineate plaques, astrocytes, and microglia.

Wang wanted to know how the amount of amyloid, and the maturity of plaques in each spot, influenced nearby neurons. Noting that plaques typically evolve from diffuse to compact to dense-core, Wang said that in these samples, spots with low levels of Aβ tended to host diffuse plaques, while those with more Aβ contained predominantly compact plaques and a few dense ones. Wang reported that neurons near diffuse, low-Aβ areas seemed to fare worse than neurons stationed near compacted plaques. Neurons adjacent to diffuse plaques had downregulated synaptic function genes and turned up apoptosis genes.

Once astrocytes and microglia were recruited to a plaque, neurons and other nearby cells appeared to be in trouble. Wang reported that in spots with high numbers of glia, neurons expressed a similar neurodegenerative profile. Spots with low Aβ and swarms of glia were the most hostile environs for neurons. Wang also tracked numerous other cell types, reporting, for example, that some subtypes of inhibitory neuron and oligodendrocyte precursor appeared most vulnerable to low-Aβ, high-glia conditions.

Wang zeroed in on the profiles of microglia near plaques, comparing them to previously reported subtypes. She found that plaque-adjacent microglia tended to be of the “ribosome biogenesis” persuasion, a subtype thought to closely resemble the disease-associated microglia (DAM) identified in mice (Oct 2023 news on Sun et al., 2023).

Wei-Ting Chen of Muna Therapeutics in Leuven has a related purpose in mind for spatial transcriptomics. She investigates how, despite their hefty burden of amyloid plaque, some people manage to fend off tau pathology and cognitive decline. Specifically, Chen wants to know which potentially protective cellular mechanisms might be at work around plaques among cognitively resilient people, or destructive ones in people with dementia. In collaboration with De Strooper’s lab at KU Leuven, Chen deployed Visium for spatial transcriptomics, single-nucleus RNA sequencing, and immunostaining for Aβ and p-tau on adjacent sections taken from cortical punches from the superior frontal gyri of 44 people. Donors came from two main autopsy cohorts (image below). One comprised octogenarians, including eight who died without amyloid pathology, eight who had amyloid plaques but no dementia, and eight who died with AD dementia. The second cohort comprised 20 cognitively resilient centenarians, who died with amyloid plaques but intact cognition.

Resilient or Not? To investigate why some people with amyloid plaques get dementia, while others stay sharp, scientists analyzed brain samples from octogenarians (left) who died with plaques but not dementia (AD-DEM) or with dementia (AD+DEM), or centenarians (right) who had plaques but not dementia. [Courtesy of Wei-Ting Chen, Muna Therapeutics.]

The researchers found only a slightly higher burden of amyloid plaques among people who died with dementia relative to resilient people. In contrast, p-tau was substantially higher among people who died with dementia relative to cognitively resilient octogenarians and centenarians (image below). Furthermore, resilient people had fewer neuritic plaques, which are known to beckon tau tangles and wreak havoc on nearby synapses.

What about cellular profiles near amyloid and tau pathology? Wang reported that regardless of whether there were tangles, amyloid plaques alone incited inflammatory gene expression in nearby cells, such as complement proteins and TREM2.

Once tangles entered the picture, transcriptional clusters of reactive astrocytes, as well as microglia expressing a suite of genes previously described as the ribosome biogenesis and HLA profiles, were dramatically more abundant near plaques, whereas a subset of excitatory neurons in cortical layer 5 became depleted. A cadre of inflammatory genes, including HLA-DRA, CD74, CD14, and SPP1, were rampant around plaques in samples with abundant tangle pathology relative to samples with only plaques. Dying with dementia had a similar effect on the cellular response to amyloid plaques as did tau pathology.

To Chen’s mind, this implies that these inflammatory responses might fuel amyloid-induced tau pathology and, ultimately, neurodegeneration and cognitive decline. Counteracting these responses early in AD pathogenesis might put a wrench in the cascade, she hopes.

Wang noted that the data produced thus far by spatial transcriptomics are but the tip of the iceberg. For example, most ROS-MAP participants harbor mixed pathologies, not just Aβ and tau. Moreover, spatial omics findings always must be functionally validated to move them beyond the realm of correlation, she added. De Strooper made a similar point. Indeed, how to draw meaning from the mountain of complex data spatial transcriptomics produces is the real challenge right now, he said.—Jessica Shugart

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References

News Citations

1. Paper Alert: Those PIGs! Spatial Transcriptomics Add Human Data 30 Jul 2020
2. High-Res Spatial Transcriptomics Offers New Views of Mouse Brain 21 Aug 2022
3. Higher-Resolution Spatial Transcriptomics Maps Mayhem Near Plaques 8 Feb 2023
4. New Atlas Charts Mouse Brain in Exquisite Detail 22 Dec 2023
5. Stunning Detail: Single-Cell Studies Chart Genomic Architecture of AD 7 Oct 2023

Paper Citations

1. Mallach A, Zielonka M, van Lieshout V, An Y, Khoo JH, Vanheusden M, Chen WT, Moechars D, Arancibia-Carcamo IL, Fiers M, De Strooper B. Microglia-astrocyte crosstalk in the amyloid plaque niche of an Alzheimer's disease mouse model, as revealed by spatial transcriptomics. Cell Rep. 2024 Jun 25;43(6):114216. Epub 2024 May 30 PubMed.
2. Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, Ding Y, Mahoney JT, Dee N, Goldy J, Melief EJ, Agrawal A, Kana O, Zhen X, Barlow ST, Brouner K, Campos J, Campos J, Carr AJ, Casper T, Chakrabarty R, Clark M, Cool J, Dalley R, Darvas M, Ding S-L, Dolbeare T, Egdorf T, Esposito L, Ferrer R, Fleckenstein LE, Gala R, Gary A, Gelfand E, Gloe J, Guilford N, Guzman J, Hirschstein D, Ho W, Hupp M, Jarksy T, Johansen N, Kalmbach BE, Keene LM, Khawand S, Kilgore M, Kirkla. Integrated multimodal cell atlas of Alzheimer's disease. 2024 Feb 15 10.1101/2023.05.08.539485 (version 3) bioRxiv.
3. Park J, Wang J, Guan W, Gjesteby LA, Pollack D, Kamentsky L, Evans NB, Stirman J, Gu X, Zhao C, Marx S, Kim ME, Choi SW, Snyder M, Chavez D, Su-Arcaro C, Tian Y, Park CS, Zhang Q, Yun DH, Moukheiber M, Feng G, Yang XW, Keene CD, Hof PR, Ghosh SS, Frosch MP, Brattain LJ, Chung K. Integrated platform for multiscale molecular imaging and phenotyping of the human brain. Science. 2024 Jun 14;384(6701):eadh9979. PubMed.
4. Sun N, Victor MB, Park YP, Xiong X, Scannail AN, Leary N, Prosper S, Viswanathan S, Luna X, Boix CA, James BT, Tanigawa Y, Galani K, Mathys H, Jiang X, Ng AP, Bennett DA, Tsai LH, Kellis M. Human microglial state dynamics in Alzheimer's disease progression. Cell. 2023 Sep 28;186(20):4386-4403.e29. PubMed.

Further Reading

News

1. Hi-Res Spatial Proteomics Uncovers Aspects of Alzheimer’s Disease Pathology 18 May 2023