GoKawiilStudy participants, ethics and clinicopathological characterization
All brain tissue was obtained from participants in the Religious Order Study and Memory and Aging Project (ROSMAP)19, the Minority Aging Research Study (MARS)20 and the Latino Core Study21. As described previously, all participants are without known dementia at enrolment and have annual clinical evaluations; participants whose brain tissue was profiled in this study also consented to brain donation. At death, the brains undergo a quantitative neuropathological assessment, and the participant’s rate of cognitive decline is calculated from the longitudinal cognitive measures, which include up to 31 yearly evaluations19. An institutional review board at Rush University Medical Center approved each study, and an institutional review board at Columbia University Irving Medical Center approved the use of the post-mortem tissue samples for molecular analysis. All participants included in the analyses presented here signed an informed consent, Anatomical Gift Act and repository consent. For this study, we selected 167 participants, including all donors from the MARS and Latino Core Study who had full pathological characterization by December 2022, and availability of fresh-frozen tissue from at least two of the three brain regions profiled (DLPFC, STG and AC). As a result, our study cohort includes diverse individuals across the full range of the pathological stages and diagnosis of AD and MCI41,42,43. A subset of demographic and clinicopathological characteristics are summarized in Fig. 1 and Extended Data Fig. 1. Pathological measures were collected as previously described44,45. We focused our analysis on the following measures:
(1) CERAD neuritic plaque score42,46: a semiquantitative measure of neuritic plaques. A neuropathological diagnosis was made of no AD, possible AD, probable AD or definite AD on the basis of semiquantitative estimates of neuritic plaque density as recommended by CERAD, modified to be implemented without adjustment for age and clinical diagnosis. A CERAD neuropathological diagnosis of AD required moderate (probable AD) or frequent (definite AD) neuritic plaques in one or more neocortical regions. Diagnosis includes algorithm and neuropathologist’s opinion, blinded to age and all clinical data. The coded values correspond to 1 (definite), 2 (probable), 3 (possible) and 4 (no AD). (2) Braak stage42: a semiquantitative measure of the severity of neurofibrillary tangle (NFT) pathology. The Bielschowsky silver stain was used to visualize NFTs in the frontal, temporal, parietal and entorhinal cortex, and the hippocampus. Braak stages were based on the distribution and severity of NFT pathology: Braak stages 1 and 2 indicate NFTs confined mainly to the entorhinal region of the brain; Braak stages 3 and 4 indicate the involvement of limbic regions such as the hippocampus; and Braak stages 5 and 6 indicate moderate to severe neocortical involvement. Diagnosis includes algorithm and neuropathologist’s opinion. (3) Neuritic plaque counts: a quantified measure determined by microscopic examination of silver-stained slides from five regions: midfrontal cortex, midtemporal cortex, inferior parietal cortex, entorhinal cortex and hippocampus. The count of each region is scaled by dividing by the corresponding standard deviation. The five scaled regional measures are then averaged to obtain a summary measure for neuritic plaque counts. (4) Diffuse plaque counts: a quantitative measure determined by microscopic examination of silver-stained slides from five regions: midfrontal cortex, midtemporal cortex, inferior parietal cortex, entorhinal cortex and hippocampus. The count of each region is scaled by dividing by the corresponding standard deviation. The five scaled regional measures are then averaged to obtain a summary measure for diffuse plaque counts. (5) Amyloid-β load47: a quantified measure of amyloid-β protein identified by molecularly specific immunohistochemistry and quantified by image analysis. The value is the percentage area of cortex occupied by amyloid-β, and the overall score is the mean score in eight regions (four or more regions per individual are needed to calculate). The square root of this final score was used for association analyses. (6) PHFtau tangle density47: a quantitative measure of neuronal NFTs, identified by molecularly specific immunohistochemistry (antibodies to abnormally phosphorylated tau protein, AT8). Cortical density (per mm2) is determined using systematic sampling. Mean of tangle score in eight regions (four or more regions are needed to calculate). The square root of this final score was used for association analyses. (7) Clinical diagnosis48: physician’s overall cognitive diagnostic category, based on all available clinical data at the time of death. All available clinical data were reviewed by a neurologist with expertise in dementia, and a summary diagnostic opinion was rendered on the most likely clinical diagnosis at the time of death. Summary diagnoses were made blinded to all post-mortem data. Case conferences including one or more neurologists were used for consensus on selected cases. For this study, the categories used were NCI (no cognitive impairment; no impaired domain), MCI (mild cognitive impairment; one impaired domain) and Alzheimer’s dementia. (8) Slope of cognitive decline: a quantitative measure based on uniform structured clinical evaluations—including a comprehensive cognitive assessment—that are administered annually to the participants and have been summarized in previous publications49,50. Scores from 19 cognitive performance tests, 17 of which were used to obtain a summary measure for global cognition, as well as measures for 5 cognitive domains of episodic memory, visuospatial ability, perceptual speed, semantic memory and working memory. The summary measure for global cognition is calculated by averaging the standardized scores of the 17 tests, and the summary measure for each domain is calculated similarly by averaging the standardized scores of the tests specific to that domain. To obtain a measurement of cognitive decline, the annual global cognitive scores are modelled longitudinally with a mixed-effects model, adjusting for age, sex and education, providing person-specific random slopes of decline (which we refer to as the slope of cognitive decline). For association analyses in this study, the negative of the slope value was used (so that higher values of the association variable correspond to steeper slopes of decline).
Generation of snRNA-seq and snATAC-seq (multiome) data
Tissue preparation
This study profiles post-mortem frozen human brain tissue isolated from the DLPFC (BA9), SPG (BA22) and AC, from individuals in the ROSMAP, MARS and Latino Core Study cohorts at Rush University. Tissue from each of these regions was dissected while frozen from flash-frozen tissue blocks at Rush University and sent to Columbia University. Working on ice throughout, we dissected out the white matter and meninges, when present. The following steps were also performed on ice: about 50–100 mg of grey-matter tissue was transferred into a Dounce homogenizer (Sigma, D8938) with 2 ml NP40 lysis buffer (0.1% NP40, 10 mM Tris, 146 mM NaCl, 1 mM CaCl 2 , 21 mM MgCl 2 and 40 U ml−1 of RNAse inhibitor (Takara, 2313B)). Tissue was gently dounced while on ice 25 times with pestle A followed by 25 times with pestle B, then transferred to a 15-ml conical tube. Next, 3 ml of phosphate-buffered saline (PBS) + 0.01% bovine serum albumin (BSA) (NEB, B9000S) and 40 U ml−1 of RNAse inhibitor were added for a final volume of 5 ml and then immediately centrifuged with a swing bucket rotor at 500g for 5 min at 4 °C. Samples were processed two at a time, the supernatant was removed and the pellets were set on ice to rest while processing the remaining tissues to complete a batch of three samples for each run of the 10x Genomics Chromium platform (see pooling information below and in ‘Data availability’). The nuclei pellets were then resuspended in 500 ml of PBS + 0.01% BSA and 40 U ml−1 RNAse inhibitor. Nuclei were filtered through 20-μm pre-separation filters (Miltenyi, 130-101-812) and counted using the Nexcelom Cellometer Vision and AO/PI stain at a 1:1 dilution with a cellometer cell counting chamber (Nexcelom, CHT4-SD100-002).
Library preparation and sequencing
To generate snRNA-seq + snATAC-seq (multiome) data, individual samples were pooled into groups of three, with each pool containing (as far as possible) one sample from each of the three regions, and balanced across population groups to minimize batch effects (https://www.synapse.org/Synapse:syn53649030). Approximately 20,000 total nuclei from three samples were pooled into a single sample and these nuclei were run on the 10x Genomics Chromium platform using the 10x multiome protocol (Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle, PN-1000283). In brief, after transposition, gel beads-in-emulsion (GEMs) were generated by combining barcoded gel beads, transposed nuclei, a master mix that includes reverse transcription (RT) reagents and partitioning oil on a Chromium Next GEM Chip J (10x Genomics; PN-2000264). Incubation of the GEMs in a thermal cycler for 45 min at 37 °C and for 30 min at 25 °C generated full-length cDNA from poly-adenylated mRNA for the gene-expression library and a Spacer sequence that enabled the attachment of barcodes to transposed DNA fragments for the ATAC library. This was followed by a quenching step that stopped the reaction. Next, GEMs were broken, and pooled fractions were recovered. Silane magnetic beads were used to purify the first-strand cDNA from the post-GEM–RT reaction mixture. Barcoded transposed DNA and barcoded full-length cDNA from poly-adenylated mRNA were pre-amplified by PCR and the products were used as input for both ATAC library construction and cDNA amplification for gene-expression library construction. Libraries were pooled and sequenced together on a NovaSeq 6000 with an S4 flow cell (Illumina) at the New York Genome Center, for a target coverage of around 645 million reads per sample for snRNA-seq and 645 million reads per sample for ATAC-seq. The snATAC-seq libraries were sequenced as follows: read 1N, 50 cycles; i7 index, 8 cycles; i5 index, 24 cycles; read 2N, 49 cycles. The snRNA-seq libraries were sequenced as follows: read 1, 28 cycles; i7 index, 10 cycles; i5 index, 10 cycles; read 2, 90 cycles.
Primary processing of sequencing data
Raw fastq files were aligned to the genome or transcriptome and quantified using the CellRanger ARC v.2.0.2 package (https://www.10xgenomics.com/support/software/cell-ranger-arc/downloads) with default parameters. snRNA-seq count files were then processed with the CellBender package v.0.2.051 (https://github.com/broadinstitute/CellBender) to remove background signal, also with default parameters. The post-processing CellBender h5 files were then used for downstream analysis and demultiplexing.
... continue reading