dataesr/scientific-paragraphs-categorization huggingface dataset:
05, t-test). The mean expression of negatively and positively correlated BRGs showed indeed decreasing and increasing expression patterns, respectively, across regions R1, R3, R5, and R6 (Fig. 2d ). Second, we used RNA-sequencing (RNA-seq) data from 88-129 individuals in the GTEx consortium  19  and selected samples of the substantia nigra (R3), amygdala (R4), anterior cingulate cortex (R5), and frontal cortex (R6). For the 883 BRGs present in the GTEx consortium, 204 out of 318 (64. 2%) negatively correlated BRGs and 475 out of 565 (84. 1%) positively correlated BRGs were differentially expressed between the two most distant regions R3 and R6 in this dataset (FC R3-R6 | > 1, BH-corrected P < 0. 05, DESeq2). The mean expression of BRGs again showed decreasing and increasing patterns, here across regions R3-R6 (Fig.
2e ). Together, this indicates that the expression patterns of BRGs in the brain are consistent across non-neurological individuals. We next hypothesized that if the identified BRGs are associated with vulnerability to PD, they are also indicative of vulnerability differences between PD patients and controls. To test this hypothesis, we used two datasets with transcriptomic measurements from brain regions covering most Braak stagerelated regions sampled from PD and iLBD patients, and nondemented age-matched controls (microarray  11  (Supplementary Table  2  and Supplementary Data 4) and RNA-seq datasets (Supplementary Table  3  and Supplementary Data 5); see the "Methods" section). First, we found more differentially expressed genes between brain regions within the same group of individuals (PD, iLBD, and control) than between conditions within the same region (Supplementary Fig.
4 ). This observation further highlights the importance of assessing expression patterns across regions rather than disease conditions  22. Next, we validated the expression patterns of BRGs, which we identified in brains of non-neurological adults from the AHBA, in both the PD microarray and RNA-seq datasets. First, we observed (again) similar patterns in non-demented agematched controls (Fig. 2f, g ). Interestingly, the increasing and decreasing expression patterns of BRGs were diminished in iLBD patients and totally disrupted in PD patients across regions involved in preclinical stages R1-R3 (Fig. 2f ). Across regions R3 and R4/R5 however, these expression patterns were preserved in PD patients (Fig. 2g ). In addition to the patterns across brain regions, we found that BRGs also captured patterns across conditions PD, iLBD, and control (Supplementary Fig.
5 ). For both PD datasets, this is most apparent within the substantia nigra (R3), where negatively correlated BRGs that had higher expression in more vulnerable brain regions also had higher expression in PD patients compared to controls. Vice versa, positively correlated BRGs that had higher expression in less vulnerable brain regions also had higher expression in controls compared to PD patients. N is the number of samples across all six non-neurological donors from the Allen Human Brain Atlas (AHBA), which are involved in the six PD Braak stages as they sequentially accumulate Lewy bodies during disease progression (Supplementary Table  1  and Supplementary Fig. 1 ). Through correlation and differential expression analysis, we identified Braak stage-related genes (BRGs) with expression patterns that are either positively (r > 0) or negatively (r < 0) correlated with Braak stages in the non-neurological brain.
These were validated in cohorts of non-neurological individuals and subsequently in PD patients and age-matched controls. To obtain a more global view of BRG expression signatures, we focused on co-expression modules of all genes and correlated the module eigengene expression with Braak stages. The resulting modules of genes were subsequently analyzed to detect common biologically meaningful pathways. To summarize, these validations showed that the expression patterns of the detected BRGs are replicated in independent datasets (UKBEC and GTEx) and indeed showed progressively disturbed patterns in iLBD and PD patients (PD microarray and PD RNA-seq datasets). This was shown for the mean of both BRGs groups (increasing and decreasing), but is also shown for individual BRGs (Supplementary Fig.
We found that modules that were negatively correlated with Braak stages were enriched for markers for all different cell-types, and linked to various functions and diseases. M39 was enriched for markers of astrocytes and endothelial cells, and the function membrane raft which plays a role in neurotransmitter signaling. M127 was enriched for microglia and neurons, and associated with   Expression (log 2 -transformed) ΔB1-B6 r < 0 r > 0 f PD microarrray dataset g Expression (CPM) Expression (CPM) r < 0 r > 0 PD RNA-seq dataset c • • • • • • • • • • • • • • • • • • • • R1 R2 R3 R4 R5 R6 R1 R2 R3 R4 R5 R6 AHBA • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • R1 R3 R5 R6 R1 R3 R5 R6 r < 0 r > 0 d UKBEC • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • R3 R4 R5 R6 R3 R4 R5 R6 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • PD iLBD control R1 R2 R3 R1 R2 R3 R1 R2 R3 7.
0 7. 1 7. 2 7. 3 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • PD iLBD control R1 R2 R3 R1 R2 R3 R1 R2 R3 6. 0 6. 2 6. 4 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • PD control R3 R4/R5 SN R4/R5 2000 3000 4000 5000 6000    2 ) and g PD RNA-seq dataset (R3: substantia nigra, R4/R5: medial temporal gyrus; number of samples in Supplementary Table  3 ). Boxplots (f, g) are shown per patient group (PD, iLBD, and control) and per brain region (Supplementary Fig. 5 ). The boxplots indicate the median and interquartile range (25th and 75th percentiles) with whiskers indicating 1. 5 times the interquartile range; outliers beyond the whiskers are plotted individually.
We applied population-specific expression analysis (PSEA)  24  to the AHBA to validate the cell-type specificity of each of the 960 BRGs. We found all 960 BRGs to be differentially expressed (BH-corrected P < 0. 05, t-test) between regions R1 and R6 after correcting for five major cell-types (neurons, astrocytes, oligodendrocytes, microglia, and endothelial cells). For example, the neuronal marker ADCY1 which was identified as a BRG remains differentially expressed between regions R1 and R6 when corrected for neurons or other cell-types (Fig. 4 ). Similarly as for BRGs, PSEA analysis on all 23 Braak stage-related coexpression modules showed significant differential expression between regions R1 and R6 which cannot be fully explained by differences in cellular composition. In the PD datasets, not all BRGs were found significant after correction for cellular composition, however smaller changes can be expected when comparing regions that are less distant (R1-R3 and R3-R4/R5).
SNCA was positively correlated with Braak stages in non-neurological brains from the AHBA, with a lower expression in regions R1-R2 and higher expression in R3-R6 (Fig. 5a-c ), which was replicated in larger cohorts of non-neurological individuals (Fig. 5d, e ). This observation suggests that lower SNCA expression indicates high vulnerability of brain regions to develop LB pathology. We further validated this concept in two cohorts of PD patients in which SNCA expression similarly increased across the medulla  oblongata (R1), locus coeruleus (R2), and substantia nigra (R3) of PD and iLBD patients, and age-matched controls. SNCA was significantly lower expressed in region R1 compared to R2 and R3 in PD and iLBD patients, but not in controls (BH-corrected P < 0. 05, t-test; Fig. 5f ). In the PD RNA-seq dataset, SNCA was significantly lower expressed in the substantia nigra (R3) compared to the medial temporal gyrus (R4/R5) in PD patients, but again not in controls (BH-corrected P < 0.
05, DESeq2; Fig. 5g ). Altogether, SNCA expression patterns could be replicated in brain regions of age-matched controls, however changes were larger between brain regions in PD and iLBD cases. We further assessed SNCA expression using PSEA in the AHBA (Fig. 5h ) and found that changes were independent of neuronal or other celltype densities when comparing different brain regions. In the PD datasets, PSEA results were scattered and did not align between the microarray and RNAseq dataset, which might be caused by the small sample sizes and the comparison of different brain regions (Supplementary Fig. 13 ). Co-expression analysis in non-neurological brains from the AHBA revealed several dopaminergic genes present in module M127. Their expression patterns were further investigated together with SNCA which is also known to regulate dopamine homeostasis  25.
GCH1, TH, and SLC6A3 (also known as DAT) were related to the functional term dopamine biosynthetic process, and SLC18A2 (also known as VMAT2) is known to store dopamine into synaptic vesicles  26. Unlike SNCA, the expression of GCH1, TH, SLC6A3, and SLC18A2 was higher expressed in regions involved at preclinical stages than those  Correlation   1 ). Meta-analysis of b SNCA correlation with Braak stages and c SNCA expression fold-change (FC) between region R1 and R6 across the six donors in the AHBA. To calculate the summary effect size (orange diamonds) from the individual effect sizes (turquoise squares), the 95% confidence intervals (CI) and weights are taken into account. The positive correlation with Braak stages was validated in datasets from two healthy cohorts, d UK Brain Expression Consortium (UKBEC; 134 donors) and e Genotype-Tissue Expression Consortium (GTEx; 88-129 donors), and f, g two PD cohorts with PD patients, incidental Lewy body disease (iLBD) patients, and non-demented age-matched controls (number of samples in Supplementary Tables  2  and 3 ).
In the PD datasets, SNCA expression was tested for differential expression between regions and conditions (red, BH-corrected P < 0. 05, t-test and DESeq2, respectively). The boxplots indicate the median and interquartile range (25th and 75th percentiles) with whiskers indicating 1. 5 times the interquartile range; outliers beyond the whiskers are plotted individually. h SNCA was still significantly differentially expressed between region R1 (black) and R6 (red) when correcting for five main cell-types with PSEA in the AHBA (BH-corrected P < 0. 05, t-test). Significant BH-corrected P-values are highlighted in red together with cell-type specific foldchanges (slope change of red line). PSEA results for PD data are shown in Supplementary Fig. 13. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 7.
Identification of transcriptomic features in regions or disease conditions may be confounded by changes in cell-type composition. We used PSEA  24  to examine the impact of this confounding factor and found that all 960 BRGs remained differentially expressed between regions R1 and R6 in the AHBA. We also applied PSEA in the two PD datasets that allowed us to examine cell-type specificity between regions as well as between disease conditions. Although it is known that gene expression varies more between regions than between disease conditions  22  , it is less clear how cell-type composition contributes to this variation. Here, we found that regional comparisons yielded more significant results than when comparing disease conditions. Therefore, BRGs also captured expression changes between patients and controls, but changes were less dependent on celltype abundance between regions than between patients and controls.
To get a full understanding of how cell-types affect PD progression, we would need single cell data that map to all six Braak stage-related regions. This would allow us to assess which celltypes might influence regional vulnerability and which genes or modules relate to these cell-type specific processes. Currently, there are many single cell datasets available, but they mainly cover limbic and cortical regions  43, 44  , which only includes regions involved in the clinical stages of PD. For the substantia nigra, the hallmark region of PD, there is currently only one human single cell dataset available derived from archived samples  45. Thus, the single cell datasets that are available now do not cover all regions in the Braak staging scheme. However, as the field of single cell analysis is growing at a fast pace and more datasets are being published, we expect that studying the role of cell-types with respect to regional vulnerability to PD in a similar way as we presented here will be possible in the near future.
Our findings on BRGs were based on regional expression differences that we analyzed using the AHBA. Although the number of AHBA donors is low, we confirmed these expression patterns in UKBEC and GTEx where the number of donors is high. Since most PD studies are limited by the availability of post-mortem brains of PD patients, the two PD datasets in our study had both low numbers of regional samples and donors. Thus, our findings on regional differences in PD patients are less reliable than our findings based on non-neurological controls. Nevertheless, they can still give an indication on how the expression of BRGs changes in brains of PD patients. This study showed that collecting more samples from multiple brain regions in post-mortem PD brains is valuable to get a better understanding of the vulnerability to PD.
Methods Allen Human Brain Atlas. To examine gene expression patterns across brain regions involved in PD, we used normalized gene expression data from the AHBA  15  , a human post-mortem microarray dataset of 3702 anatomical brain regions from six non-neurological individuals (five males and one female, mean age 42, range 24-57 years). We downloaded the data from http://human. brain-map. org/. To filter and map probes to genes, the data were concatenated across the six donors. We removed 10,521 probes with missing Entrez IDs, and 6,068 probes with low presence as they were expressed above background in <1% of samples (PA-call containing presence/absence flag 15 ). The remaining 44,072 probes were mapped to 20,017 genes with unique Entrez IDs using the collapseRows-function in R-package WGCNA v1.
These brain regions corresponded to Braak stage-related regions R3-R6, respectively. We downloaded gene read counts (v7) for differential expression analysis and gene transcript per million (TPM) expression values (v7) for visualization. Out of 56,202 genes, we selected 19,820 protein coding genes and removed 405 genes with zero counts in one of the four regions of interest; 19,415 genes were left for analysis. PD microarray dataset. In the PD microarray dataset, samples were collected from the medulla oblongata (R1), locus coeruleus (R2), and substantia nigra (R3) from PD patients (67. 6% males, mean age 78, range 61-87 years), iLBD patients (42. 4% males, mean age 80, range 56-98 years), and non-demented controls (54. 5% males, mean age 77, range 60-91 years) (Supplementary Table  2  and Supplementary Data 4).
The PD microarray data of the substantia nigra (R3) was previously published in Dijkstra et al. 11  (GEO accession number GSE49036). Based on pathological examination, PD patients in the microarray dataset revealed LB pathology in accordance with Braak stages 4-6, and iLBD patients showed LB pathology in the brainstem (Braak stages 1-3), and therefore represent the early stages of PD. Additional samples from the medulla oblongata (R1) and locus coeruleus (R2) were collected and processed of the same cohort in the same manner for hybridization on GeneChip® Human Genome U 133 Plus 2. 0 arrays. Probe IDs were mapped to Entrez IDs with the mapIds-function in the hgu133-plus2. db R-package v3. 2. 3. We removed 10,324 out of 54,675 probes with missing Entrez IDs. The remaining 44,351 probes were mapped to 20,988 genes with unique Entrez IDs using the collapseRows-function similarly as was done for the AHBA.
PD RNA-sequencing dataset. In the PD RNA-seq dataset, samples from the substantia nigra (R3) and medial temporal gyrus (R4/R5) were collected from PD patients (61. 1% males, mean age 79, range 57-88 years), and non-demented age-matched controls (48. 0% males, mean age 78, range 59-93 years) (Supplementary Table  3  and Supplementary Data 5). The extracted RNA was quantified using an Ozyme NanoDrop System, of which 500 ng of total RNA from each sample was further processed for purification of ribosomal (rRNA) using human Illumina Ribo-Zero™ rRNA Removal Kit. Then the Illumina TruSeq stranded total RNA protocol was used for library preparation. The library was sequenced on a Hiseq4000. RNA-seq reads were aligned to human genome (GRCh 38) with TopHat software (version: 2. 1. 1) using reference gene annotations (Ensembl GRCh38.
Differential gene expression in validation datasets. A two-sided unpaired ttest was used to assess expression differences between conditions (PD, iLBD, and age-matched controls) and brain regions (R1-R6) in the AHBA, UKBEC, and PD microarray dataset. For GTEx, we used DESeq2 version 1. 22. 2  48. For the PD RNA-seq dataset, normalization and differential expression was done with 'DESeq2' R-package version 1. 10. 1, with age and sex introduced in the statistical model to take into account possible biases. Each analysis done with DESeq2 used a two-sided Wald test. The cut-off for differentially expressed genes was P < 0. 05 (BH-corrected). For microarray experiments, the FC was interpreted as the difference in mean expression µ B -µ A , with µ as the mean expression in either group A and B. For RNA-seq experiments, FC is the log 2 FC obtained from DESeq2.
Cell-type specific analysis. To assess whether results were confounded by celltype composition in different brain regions and conditions, we applied PSEA  24  in the AHBA, PD microarray dataset, and PD RNA-seq datasets. Data from the AHBA were first concatenated across the six donors before applying PSEA. This method applies linear regression to examine whether the expression between two groups of samples is different (two-tailed t-test) while correcting for cell-type composition estimated from cell-type markers. To define cell-type markers, we used gene expression data from sorted cells of the mouse cerebral cortex  23. Genes were selected as markers when they had a 20-fold higher expression compared to the mean of the other cell-types. All genes were analyzed while correcting for five main cell-types for which the cell-type signal was estimated by taking the mean expression of markers: 628 neurons, 332 astrocytes, 186 oligodendrocytes, 520 microglia, and 456 endothelial cells.
P-values were BH-corrected across all genes in a dataset and significant when <0. 05. Gene co-expression modules in Braak stage-related regions. Gene coexpression matrices (pairwise Pearson's correlation, r, across Braak stage-related regions R1-R6) were calculated for each one of the six brain donors in the AHBA separately, and then combined into one consensus matrix based on the elementwise mean across all donors. Co-expression was converted to dissimilarity based on 1-r; in this way only positively co-expressed genes were taken into account. All genes were hierarchically clustered using single, complete, and average linkage and co-expression modules were obtained with the cutreeDynamicTree-function in the dynamicTreeCut R-package 1. 63; minimum module size was set to 50 by default. The weighted correlation network analysis (WGCNA) R-package version 1.
Gene set enrichment analysis of Braak stage-related modules. The one-sided hypergeometric test was used to identify modules that are significantly enriched for BRGs, cell-type markers  23  , gene ontology-(GO), and disease-associated genes from DisGeNET  49. A table of 561,119 gene-disease associations were obtained from DisGeNET version 5. 0 (May, 2017) from http://www. disgenet. org/. Gene sets associated with 17,857 GO-terms were obtained from the Ensembl dataset hsa-piens_gene_ensembl version 92 through biomaRt R-package version 2. 38. All gene sets were filtered to contain only genes matching the 20,017 genes in the AHBA and at least 10 genes. Modules were significantly enriched when P < 0. 05 (BHcorrected for number of modules and gene sets) using all 20,017 genes from the AHBA as background genes.
Acknowledgements We thank  S. M. H. Huisman  and prof. J. J. Goeman  for their support in the statistical analyses. We also want to thank  G. Bonvicini  for her help in accessing the RNA-seq data from PD patients and  V. Bonifati  for critical discussions during the preparation of the manuscript. This research received funding from The  Netherlands Technology Foundation (STW) , as part of the  STW  project  12721  ( Genes in Space, PI Lelieveldt ). W. D. J. v. d. B. and M. -C. C. -H. received funding from  Alzheimer Netherlands  and  LECMA/Vaincre Alzheimer  to collect the RNA-sequencing datasets that are used in this study. W. D. J. v. d. B. was financially supported by grants from  Amsterdam Neuroscience, Dutch Research council (ZonMW) ,  Stichting Parkinson Fonds , Alzheimer association, and  Rotary Aalsmeer-Uithoorn.
Simulation setup and mock data As current measurements are not adequate in terms of joint spatial and spectral resolution to build a representative toy model and perform a feasibility study of the afore-presented science case by X-IFU, this study relies on input numerical simulations, which are then converted into mock X-IFU datasets. The full method is described extensively in Paper I (to which we refer the reader for more details). In summary, four Lagrangian regions are extracted from a parent large-scale cosmological simulation and re-simulated at higher resolution including the treatment of hydrodynamical processes with sub-grid physics models (using the tree-PM smoothed particle hydrodynamics code GADGET-3; see  Rasia et al. 2015). Of these four systems, two are 'cool-core' and two are 'non-cool-core', and each class contains both a low-and a high-mass cluster.
(  7 ) for a SN Ia , a SN cc , a AGB (based on the approach of  Ettori et al. 2002 , Gastaldello & Molendi 2002 , Werner et al. 2006 , de Plaa et al. 2007 , Mernier et al. 2016b , and Simionescu et al. 2019 ). The fit is then performed under the constraint: a AGB Y Fe,AGB + a SN cc Y Fe,SN cc + a SN Ia Y Fe,SN Ia = 1. ( 8 ) 3 https://github. com/mernier/abunfit The full list of models (along with their relevant specifics) used for this comparison, and covering a significant fraction of the recent literature, is detailed in Appendix A. Most AGB and SN cc models are tested individually over the available range of initial metallicities Z init. Among the tested SN cc models, we also include a set of hypernova and pair-instability SN predicted yields in order to explore the reproducibility of the abundance pattern through an enrichment from very massive, metalpoor stars.
Best fits and extension to higher redshift The results shown above offer very promising perspectives for the X-IFU. Through simple linear fits and physical considerations, we are able to accurately recover the underlying enrichment model implemented in the hydrodynamical simulations. As shown in Fig. 1  and Sect. 3. 1, the best fit for the data set is obtained, at z = 0. 1, for Z init = 0. 01 Z for SN cc yields and Z init = 1 Z for AGB yields, with values of the reduced chisquared of 1. 49 for local clusters, nicely recovering the input yield models from the simulations. The previous approach is also extended at z = 1 with excellent results (see Fig. 3 ). We find that the X-IFU is still capable of accurately recovering abundance ratios within R 500 and the underlying input combination of models (same as Sect.