Last updated: 2023-12-22
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Knit directory: 1_heteroAstrocytes/
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Ignored: transfers.RData
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/methods.Rmd) and HTML
(docs/methods.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 656a78d | Evgenii O. Tretiakov | 2023-12-22 | fix small typos |
| Rmd | 9e9b929 | Evgenii O. Tretiakov | 2023-12-21 | update methods to the manuscript and designed figures |
| html | 9e9b929 | Evgenii O. Tretiakov | 2023-12-21 | update methods to the manuscript and designed figures |
| Rmd | e1f31e6 | Evgenii O. Tretiakov | 2023-12-15 | submit |
# Presentation
library("glue")
library("knitr")
library("reticulate")
# JSON
library("jsonlite")
# Tidyverse
library("tidyverse")
source(here::here("code", "functions.R"))if (!file.exists(here::here("output/methods/packages.bib"))) {
dir.create(here::here("output", DOCNAME), showWarnings = FALSE)
write_bib(
c(
"base", "Seurat", "SeuratWrappers", "SeuratDisk", "sctransform",
"glmGamPoi", "patchwork", "scCustomize", "Nebulosa", "clustree",
"mrtree", "gprofiler2", "cowplot", "UpSetR", "ggstatsplot",
"gridExtra", "tidyverse", "dplyr", "tidyr", "magrittr", "stringr",
"skimr", "future", "purrr", "here", "workflowr", "zeallot", "knitr",
"kableExtra", "rmarkdown", "reticulate", "uwot", "Rtsne", "MAST"
),
file = here::here("output/methods/packages.bib")
)
}versions <- list(
biomaRt = packageVersion("biomaRt"),
cellbender = "docker://etretiakov/cellbender:v0.0.1",
cellranger = "7.1.0",
cellrank = reticulate::import("cellrank")$`__version__`,
cellranger_ref = "mm10_optimized_v.1.0",
clustree = packageVersion("clustree"),
cowplot = packageVersion("cowplot"),
dplyr = packageVersion("dplyr"),
future = packageVersion("future"),
ggplot2 = packageVersion("ggplot2"),
ggstatsplot = packageVersion("ggstatsplot"),
glmGamPoi = packageVersion("glmGamPoi"),
gprofiler2 = packageVersion("gprofiler2"),
gridExtra = packageVersion("gridExtra"),
here = packageVersion("here"),
quarto = packageVersion("quarto"),
kableExtra = packageVersion("kableExtra"),
knitr = packageVersion("knitr"),
leidenalg = reticulate::import("leidenalg")$version,
liana = reticulate::import("liana")$`__version__`,
magrittr = packageVersion("magrittr"),
MAST = packageVersion("MAST"),
mrtree = packageVersion("mrtree"),
Nebulosa = packageVersion("Nebulosa"),
pacmap = reticulate::import("pacmap")$`__version__`,
pandoc = rmarkdown::pandoc_version(),
patchwork = packageVersion("patchwork"),
purrr = packageVersion("purrr"),
pyroe = "docker://combinelab/usefulaf:0.9.3",
python = py_config()$version_string,
R = str_extract(R.version.string, "[0-9\\.]+"),
reticulate = packageVersion("reticulate"),
rmarkdown = packageVersion("rmarkdown"),
scanpy = reticulate::import("scanpy")$`__version__`,
scCustomize = packageVersion("scCustomize"),
sklearn = reticulate::import("sklearn")$`__version__`,
sctransform = packageVersion("sctransform"),
scvelo = reticulate::import("scvelo")$`__version__`,
Seurat = packageVersion("Seurat"),
simpleaf = "docker://etretiakov/usefulaf:0.9.0",
skimr = packageVersion("skimr"),
stringr = packageVersion("stringr"),
Snakemake = reticulate::import("snakemake")$`__version__`,
scrublet = "0.2.3",
tidyr = packageVersion("tidyr"),
tidyverse = packageVersion("tidyverse"),
UpSetR = packageVersion("UpSetR"),
uwot = packageVersion("uwot"),
viridis = packageVersion("viridis"),
workflowr = packageVersion("workflowr"),
xgboost = reticulate::import("xgboost")$`__version__`,
zeallot = packageVersion("zeallot")
)Downloading and preprocessing single-cell RNA-seq data: Raw sequencing data files were obtained from the NCBI Gene Expression Onimbus mirror of the European Bioinformatics Institute using the IBM Aspera tool-kit (example command: ascp -QT -l 800m -k 1 --overwrite=diff -P 33001 -i ~/asperaweb_id_dsa.openssh era-fasp@fasp.sra.ebi.ac.uk:/path/to/file/on/server) or directly from the NCBI mirror using the ncbi-toolkit based on the BioProject ID (example command: esearch -db sra -query PRJNA****** | efetch -format runinfo | cut -d ‘,’ -f 1 | grep SRR | xargs -n 1 -P 20 prefetch --max-size u && esearch -db sra -query PRJNA****** | efetch -format runinfo | cut -d ‘,’ -f 1 | grep SRR | xargs -n 1 -P 20 fasterq-dump -p -x --threads 10 --mem 20000M --outdir fastq --split-files --include-technical && pigz -p 20 fastq/SRR*.fastq). If an original bam-file from the 10X Genomics Cell Ranger count pipeline was deposited, it was further converted to fastq (example command: ~/src/cellranger-7.1.0/bin/cellranger bamtofastq --nthreads=12 /data/PRJNA******/bam/SRR*******.bam /data/PRJNA******/fastq/SRR*******/). Information regarding publicly available data deposition are in ED Table 1.
Deriving initial UMI-count matrices: The Cell Ranger pipeline (v7.1.0)(Zheng et al., 2017) was used for sample demultiplexing, barcode processing, and single-nucleus gene counting. Reads containing sequence information were aligned using the optimized mouse genome reference (vmm10_optimized_v.1.0) provided by Pool’s lab(Pool et al., 2022), which is based on the default Cell Ranger mm10 genome version 2020-A that was cleared from gene overlaps, poorly annotated exons, 3’-UTRs, and intergenic fragments. PCR duplicates were removed by selecting unique combinations of cell barcodes, unique molecular identifiers (UMIs), and gene IDs. Thus, a gene expression matrix was created and used for further analysis. We aligned reads using ‘--include-introns’ quantification mode.
RNA-velocity for count matrices: To obtain multiple count matrices for each read source (related to exon/intron gene structures), we have built a piscem index(He, Soneson and Patro, 2023) of the reference genome for the spliceu version of the simpleaf quantification pipeline (vdocker://etretiakov/usefulaf:0.9.0) based on alevin-fry(He et al., 2022). This approach achieved the high-quality resolution of any ambiguity of the read source, which was particularly important in case of single-nuclei RNA-seq(Eldjárn Hjörleifsson et al., 2022; Kuo, Hansen and Hicks, 2022; He, Soneson and Patro, 2023), which can strongly affect the outcome of RNA-velocity estimates(Soneson et al., 2021; Gorin et al., 2022).
Preparation of individual single-cell RNA sequencing datasets for analysis
1. Droplet selection: The droplet selection method of Cell Ranger is based on the EmptyDrops method(Lun et al., 2019) incorporated into the Cell Ranger count pipeline. We refer to ED Table 1 for Information on the number of detected cells/nuclei.
2. Ambient RNA removal: We applied CellBender, a neural network-based approach (docker://etretiakov/cellbender:v0.0.1)(Fleming et al., 2023). To derive additional information on the degree of regionally different levels of contamination and the quality of particular samples, we set false positive rate thresholds at successive levels (0.1, 0.01, and 0.001) and powered the neural network to learn over 150 epochs with a total number of droplets included on knee plots. Our subsequent analysis was performed at all three resolutions and without correcting data variants. We refer to ED Table 1 on the size of droplet sets used.
3. Doublet detection: For each sample, we separately quantified the probability of a cell being a doublet based on the expected doublet-rate reference table provided by 10X Genomics, and the number of cells/nuclei in the samples by Scrublet (v0.2.3@pyh5e36f6f_1)(Wolock, Lopez and Klein, 2019). Information on the expected doublet rates used in this estimation are in ED Table 1. (see also: GetDoubletRate in function.R and scrublet_cb.py files of the code directory).
4. Further filtering: Information on gene annotation was added by using the gprofiler2 package (v0.2.1)(Reimand et al., 2016). Accordingly, we filtered cells based on their high content of mitochondrial, ribosomal, or hemoglobin genes. Specific thresholds were chosen individually for each dataset (see: GitHub Exploratory Analysis reports and params.json files). Additionally, pseudogenes and poorly annotated genes were deleted from the count matrix. Cells of low complexity were filtered out, too (as log_10 Genes/log_10 UMI). Cells were then assigned cell cycle scores using the CellCycleScoring function in the Seurat package (v4.3.0)(Satija et al., 2015; Stuart and Satija, 2019). For correlation analysis of astrocytes in ARC(Deng et al., 2020), we filtered cells based on the expression of 13 mitochondrially coded genes, using a strict approach to avoid bias caused by the technical variability of sequencing when exploring Mfn2 in particular. To do so, we applied individual filtering models fitted for each sample of the dataset separately(Grün and Leisch, 2008; Hastie, 2017; Hippen et al., 2021) (for packages splines, flexmix, miQC): 536-1_chow-diet, 536-3_chow-diet, 537-5_538-2_high-fat-diet - mixtureModel with posterior cutoff 0.95; 536-5_chow-diet - spline mixtureModel with posterior cutoff 0.7, 537-1_537-3_high-fat-diet - spline mixtureModel with posterior cutoff 0.95, 536-2_537-4_high-fat-diet - spline mixtureModel with posterior cutoff 0.999999.
Astrocyte selection
The analysis described here were performed twice, with the 1st iteration exploring all cell types, while the 2nd focusing on particular glial cell types: i) astrocytes, ii) tanycytes, iii) ependymal cells and NG2-glia, and iv) oligodendrocyte precursors (ODPs).
1. Gene selection: The selection method of the Seurat package (v4.3.0)(Satija et al., 2015; Stuart and Satija, 2019), which uses a modern variance of stabilizing the transformation of statistical technics that utilizes the scaling to Pearson residuals(Hafemeister and Satija, 2019). Thus, we selected 3,000 highly variable genes per dataset, and regressed out complexity and cell-cycle variability prior to the final scaling of the filtered matrixes.
2. Graph-based and multi-level reconcile tree clustering: We performed Leiden algorithm graph-based clustering. PCA was performed using selected genes. Jackknife tested(Chung and Storey, 2015) principal components (we tested the significance of feature for randomly picked in 100 samples by 2% of data, each over 1,000 iterations; see PCScore function in the functions.R script of the code directory) allowed us to construct a shared nearest neighbor graph by relying on the overlap amongst the 15 nearest neighbors of each cell. Leiden modularity optimization(Traag, Waltman and van Eck, 2019) was used to partition this graph with an array of resolution parameters, where 30 modularity events were sampled between 0.2 and 2.5. Visualizations of clustering trees(Zappia and Oshlack, 2018) were produced using the clustree package (v0.5.0), which showed the resolution of previously identified clusters. By inspecting these resolutions, a reconciled tree was produced by the mrtree package (v0.0.0.9000)(Peng et al., 2021), including a calculation of an adjusted multi-resolution Rand index, which was then chosen as the maximum value if there was no higher modularity within an additional 0.05 AMRI difference (see: SelectResolution in the function.R file of the code directory).
3. Marker genes: Marker genes for each cluster were identified using the logreg test(Ntranos et al., 2019) implemented in the Seurat framework (v4.3.1)(Hao et al., 2021). Genes were considered significant markers for a cluster if they had an FDR of < 0.001. Identities were assigned to each cluster by comparing the detected genes to previously published markers and our own validation experiments. We assigned the ‘astrocyte’ label to a cell only if it contained at least n = 7 marker genes. The list of n = 22 marker genes that we used as inclusion criteria are referred to in ED Table 2 (see: class_cello.py files of the code repository).
Classification of cell types based on gene-scoring based on information from publicly available databases: We further explored gene signatures using enrichment with over-representation analysis(Badia‑I-Mompel et al., 2022). To this end, we used canonical markers from PanglaoDB with particular focus on i) astrocytes, ii) tanycytes, iii) ependymocytes and ODPs (see: class_cello.py file of the code repository).
Filtering criteria for astrocytes: Astrocytes were filtered with cell quality markers listed in the code repository as individual params.json files for each dataset. Regardless of the dataset, and the selection method of astrocytes, we further filtered cells using a manually curated list of 59 marker genes for each major subtype of cells (glutamatergic, GABAergic and peptidergic neurons, OPCs, mature myelinating oligodendrocytes, pericytes, vascular muscle cells, macrophages, microglia, tanycytes, and ependymocytes. Thus, any astrocyte to pass our filter ought to contain a maximum of n = 2 genes from the list of exclusion criteria as strict trade-off, also taking into account the effect of ambient RNA (ED Table 1). Marker genes used as exclusion criteria were listed in ED Table 2 (see: class_cello.py file of the code repository).
Prepare, train, and test data splitting to enable hyperparameter optimization for supervised machine learning models by classification performance metrics: As we had variable numbers of input cells in subregional datasets, we defined the probability of a cell to be in the training set as 90%, or if this percentage was smaller than the ratio of 1,000 cells/the total cell number in the dataset. We used this probability to randomly assign cells to the training set. We used this process to obtain more balanced training sets regardless of the initial number of astrocytes in a hypothalamic region. We used the same training sets for all models to ensure that the results were comparable. The remaining cells were assigned to the test set to enable the evaluation of the model’s performance on unseen data. We used the same test sets for all models to ensure comparable results (see: class_cello.py file of the code repository). As a result of the previous steps, we derived subsets of astrocytes from each of 12 individual subregional dataset, split into train/test sets and 4 ‘whole hypothalamus’ datasets (ED Fig. 4a/Step 0).
Identification of astrocyte signatures and diversity description across hypothalamic subregions
Full conventional integration: We first performed data integration using well-established anchor-based integration using the Seurat package (v4.3.0) to compare astrocytes from different hypothalamic areas(Satija et al., 2015; Hao et al., 2021). We used both SCTransform-based and GLM-scaled versions of the default pipeline aimed to explore astrocyte heterogeneity with varying numbers of highly variable genes selected (2,500—7,500; initial hvg param). A particular problem we have encountered was the shallow fraction of variability shared across all analyzed hypothalamic regions (no more than 426 genes), which mostly were general astrocyte markers common across the nervous system.
Differential gene expression (DGE) analysis on SCTransform-corrected UMI-count matrices was statistically evaluated to obtain differentially expressed genes using log-normalized values with pseudocount = 1 for n = 12 regional hypothalamus clusters using the MAST test(Finak et al., 2015), as described(Mayer et al., 2018; Hafemeister and Satija, 2019). Results were provided in ED Table 3.
Iterative paired integration while masking shared cellular signatures: We used an alternative strategy in which shared signatures of astrocytes were reduced to address if any level of regional heterogeneity could be uncovered behind astrocytes transiting across functional states. To avoid that rapidly reducing the number of anchor genes used to integrate datasets would become a limitation of precision, we have preferred the pair-wise integration of each subregional dataset with each ‘whole hypothalamus’ dataset using Python implementation of the Harmony algorithm(Korsunsky et al., 2019). To define regional differences amongst astrocytes, rather than functional state-changes (as above), anchor genes (defined as a subset of highly variable features corrected for concatenated datasets to maximize the stringency of a variance-stabilizing transform procedure(Lause, Berens and Kobak, 2021) optimized on the earlier pair-wise integrations) were extracted, with 48 sets of 2,000 genes in total or 12 sets (each for a hypothalamic nucleus)(Moffitt et al., 2018; Mickelsen et al., 2019, 2020; Deng et al., 2020; Wen et al., 2020; Affinati et al., 2021; Lopez et al., 2021; Morris et al., 2021; Liu et al., 2022; Lutomska et al., 2022; Pool et al., 2022) per a reference set of the ‘whole’ hypothalamus(Zeisel et al., 2018; Kim et al., 2020; Romanov et al., 2020; Hajdarovic et al., 2022) (see: get_full_pair_mtx.py file of the code repository; ED Fig. 3a; PaCMAP embedding of integrated datasets before ambient RNA removal as in ED Fig. 4a/Step 1). Thus, a list of genes ordered by their descending variance on the same plane of the whole hypothalamus reference was extracted and adapted to a rank aggregation algorithm(Kolde et al., 2012) to distinguish broadly- expressed genes associated with functional cell-states: i) we derived scores per a ‘whole’ hypothalamus reference dataset, accounting for genes in the mouse genome version as above (45,163 genes in initial matrixes; see: get_shared_signature.R file of the code repository; ED Fig. 4a/Step 2). ii) We estimated rank aggregation scores of 4 derivate lists (ED Table 3; see: get_aggregated_shared_signature.R file of the code repository; ED Fig. 4a/Step 3).
Consequently, we derived integrated manifolds between 12 area-specific(Moffitt et al., 2018; Mickelsen et al., 2019, 2020; Deng et al., 2020; Wen et al., 2020; Affinati et al., 2021; Lopez et al., 2021; Morris et al., 2021; Liu et al., 2022; Lutomska et al., 2022; Pool et al., 2022) and four ‘whole’ hypothalamus datasets(Zeisel et al., 2018; Kim et al., 2020; Romanov et al., 2020; Hajdarovic et al., 2022), again after removal of shared signatures using n = 100, 250, and 500 genes as thresholds (redundant correlated genes were also removed by absolute Pearson’s correlation with> 0.5 corresponding to each threshold; ED Fig. 4a/Steps 4-5) to estimate the impact of function-specifying genes (see: get_substr_pair_mtx.py file and particularly get_top_abs_correlations() and remove_irrelevant_top_abs_correlations() functions in the code repository; ED Fig. 4a/Steps 6-7). These steps were used to i) unmask potentially hidden spatial marks in astrocytes from different hypothalamic nuclei, ii) clean the genes axis of the expression matrices, and iii) have their respective manifolds dimensionally reduced by the integration.
The downstream analysis of the subregional heterogeneity of astrocytes reported here used datasets after removal of ambient RNA at 0.001 FDR, as well as of a shared signature of n = 100 genes (as threshold) with correlated genes as described above, and using k = 10 to construct graphs for nearest neighbors (NN; ks = k + 10 = 20 for expanded mutual kNN-graphs with a path-connectivity model) as our limited testing (FDR=[0.001, 0.01, “nc”]; signature_substruction=[100, 250, 500, “full”], neighbors_k=[5, 10, 25, 50], connectivity_mst=[“full_tree”, “min_tree”]) demonstrated best performance. We also had to reduce complexity (see Snakefile in the root of the main GitHub repository). To explore relevant parameters combinations, we used a simplified approach: i) we performed over-clustering of the training set of each integrated pair using clustering from leidenalg (v0.9.1@pypi_0)(Traag, Waltman and van Eck, 2019), ii) subset each of the 12 matrices to the same reference dataset(Hajdarovic et al., 2022), iii) derived a fuzzy simplicial set using UMAP-based path-connectivity(McInnes et al., 2018; Dalmia and Sia, 2021), iv) aggregated results (microclusters and graphs) by partitioning-based graph abstraction(Wolf et al., 2019), v) PAGA graphs were utilized as layers of multiplexed graphs to optimize multiplex partitions(Mucha et al., 2010; Traag, Waltman and van Eck, 2019), vi) used those partitions to train a support vector machine (SVM) classifier (SVC)(Guyon et al., 2002; Chang and Lin, 2011) and to project those partitions on test sets astrocytes, and vii) embedded a full reference dataset labels for supervised densMAP(Narayan, Berger and Cho, 2021) data embedding using derived (see: microclustering_limited_test.ipynb file of the analysis repository; an example of unfiltered (FDR=“nc”, signature_substruction=“full”, k=10, ks=20, connectivity_mst=“min_tree”) (Fig. 1K,M).
Area-specific hypothalamic astrocytes revealed by AstroTRAP
Feature selection in 2 steps: We prefiltered features using methods to capture genes of known subregional origin (ED Fig. 4a/Step 11). To this end, we used a union of gene sets selected by χ2 statistics(Pearson, 1900), ANOVA F-value(Fisher, 1992), and mutual information score(Kraskov, Stögbauer and Grassberger, 2004). We coincidently used these feature selection methods to improve the accuracy and stability of the selected features, leading to better performance in machine learning models. We used GridSearchCV to optimize parameters of the pipelines that consisted of 3 steps: ‘scaling’: MinMaxScaler for consistency (as negative values were not allowed in our calculations), ‘reduce dimensionality’: SelectKBest taking input one ‘classify’ (χ2, f_classif or mutual_info_classif): SVC estimator with scaled gamma and a maximum number of iterations fixed to 1,000. The parameter grid included SVM fit strength (C_OPTIONS = [1, 10, 100, 1000]), and a number of features (‘K’) to select with genes thresholds (2,000, 1,000, 500, 250)(Pedregosa et al., 2011). Then, we used RepeatedStratifiedKFold(n_splits=10, n_repeats=10) cross-validation to select classifiers with the highest weighted F-measure scores. Finally, we extracted features from each best_estimator and merged them into one set of genes (ED Fig. 4a/Step 11; ED Table 4). The union of feature sets allowed us to filter input to the downstream pipeline.
Best SVM classifier model with ad hoc feature selection for region-label assignment using Grid-Search grouped k-fold cross-validation: To build an effective classifier model that reconciles features of subregional origin regardless of functional cell-states (that were captured as shared signatures), we used astrocytes pre-selected from area-specific datasets (ED Fig. 4a/Step 0), and pre-filtered gene sets (ED Fig. 4a/Step 11). To learn features that consistently represent regional heterogeneity, we recognized stand-alone functional roles of nuclei/areas of the hypothalamus. Functional state-differences amongst astrocytes might occur across the whole hypothalamus due to environmental and/or neuronal influences. Thus, we hypothesized that astrocytes are not specific to a given region at the long term; instead undergo continuous complex trajectory transitions. To resolve these problematics, we clustered their shared structure into tiny groups of cells that were being examined separately. Thus, we inferred a reduction in intra-regional specificities and could focus on inter-regional characterization at the transcriptome (gene) level. After having reduced number of genes and meaningful groups for stratification, we applied feature selection pipelines from ‘scaling’ RobustScaler that smooth outliers, feature selection, and classification by SVC. We implemented two types of feature selection:LogisticRegression that utilized L1-regularisation to fit coefficients for a multiclass problem vs. rest (for the sake of computational performance in contrast to multinomial estimation); and the XGBoost classifier. To fit these models, we encoded region classes as sorted proportions of each class. To select the best model, we performed GridSearchCV with StratifiedGroupKFold(n_splits=10) cross-validation on the training set scored by the Matthews correlation coefficient (MCC)(Chicco, Tötsch and Jurman, 2021), as regional class proportions were unbiased.
Identifications of astrocyte clusters beyond functional states: To further consolidate the signal we have gained, graph representations were generated using a minimal spanning tree also implementing A path-connectivity model of the UMAP algorithm(McInnes, Healy and Melville, 2018).Firstly, we reduced 12 integration manifolds of training data(Hajdarovic et al., 2022), using the UMAP algorithm(McInnes et al., 2018) with following parameters: n_components=6, n_neighbors=20, n_epochs=1000, metric=“cosine”, init=“spectral”, learning_rate=0.1, min_dist=1, spread=2, repulsion_strength=2.0, negative_sample_rate=10, angular_rp_forest=True, and densmap=False. Secondly, the UMAP algorithm drove the 10-NN graph from this matrix in Euclidean space with pynndescent optimization. Thirdly, a 20-NN graph and distance matrix was used to build a 20-mutual-NN graph using the minimal tree path-connectivity model (functions: min_spanning_tree(), create_connected_graph(), find_new_nn(), mutual_nn_nearest() in microclusters_to_groups_cv_feature_selection_svc.ipynb)(Dalmia and Sia, 2021). Fourthly, we derived a fuzzy simplical set for the ensuing graph using the UMAP algorithm(McInnes, Healy and Melville, 2018). Graph information were saved in an anndata container by being transformed from_scipy_sparse_array to iGraph, and through igraph.Graph.get_adjacency_sparse() to adata.obsp[“connectivities”] object. We also derived the required adata.obsp[“distances”] using the get_sparse_matrix_from_indices_distances_umap() function (see: microclusters_to_groups_cv_feature_selection_svc.ipynb file in the main GitHub analysis repository; ED Fig. 4a/Step 8)(Csardi and Nepusz, 2006; Hagberg, Swart and Chult, 2008; Virshup et al., 2023). Next, we used Optimiser class from the leidenalg package to explore the resolution profile of the Constant Pots Model clustering on the weighted graph at a range from 0 to 2, until full convergence(Traag, Waltman and van Eck, 2019). Thus, we partitioned the weighted graph using the CPMVertexPartition function with resolution_parameter=0.0112, and applied the optimise_partition function of the optimizer until convergence(Traag, Waltman and van Eck, 2019). The partitioning membership was saved as a categorical vector to the PRJNA779749_init.obs[“subfunct_groups”] (ED Fig. 4a/Step 9). Thus, we trained 12 support vector machine classifiers (linear kernel, svm fit strength - C=100) from the sklearn package to fit corresponding labels (PRJNA779749_init.obs[“subfunct_groups”]) to the subsets of 12 integrated matrices. Therefore, we projected learned group labels to the corresponding regional parts of the training sets (ED Fig. 4a/Step 10).
Optimization of hyperparameters for a logit-SVM classifier model: We trained our pipeline with SelectFromModel(LogisticRegression(solver=“saga”, multi_class=“ovr”, penalty=“l1”, max_iter=10000, n_jobs=-1,), threshold=-np.inf) feature selection and SVC estimator with scaled gamma and a maximum number of iterations fixed at 10,000. The parameter grid included SVM fit strength (C_OPTIONS = [1, 10, 100, 200]) and maximal number of features to select with N_FEATURES_OPTIONS = [round(n_features / 3), round(n_features / 4), round(n_features / 8), round(n_features / 10), round(n_features / 20)] (where n_features equals the number of genes in the union of prefiltered sets). We have also extracted features from best_estimator (ED Table 4). The best model was then trained on the whole training set, and then stored (ED Fig. 4a/Step 12).
Optimization of hyperparameters for the XGBoost-SVM classifier model: Our pipeline was trained with SelectFromModel(XGBClassifier(tree_method=“gpu_hist”), threshold=-np.inf) feature selection(Chen and Guestrin, 2016) and SVC estimator with scaled gamma and a maximum number of iterations fixed at 10,000. The parameter grid included SVM fit strength (C_OPTIONS = [1, 10, 100, 200]). At the feature selection step, we have optimized parameters of the estimator as learning_rate”: [0.1, 0.3, 0.5], boosting trees depth and number of rounds at max_depth: [3, 5, 7], n_estimators”: [100, 250, 500], and the maximal number of features to select as N_FEATURES_OPTIONS (see logit-SVC section). We have also extracted features from best_estimator (ED Table 4). Finally, we trained the best model on the whole training set, and stored it for further evaluation (ED Fig. 4a/Step 12).
Evaluation of the best classifier model: Based on the above optimization, we have built two SVM-based classification pipelines, used on our test dataset with additional validation on ‘whole’ hypothalamus predictions: i) LogisticRegression (logit)-based feature selection with L1-regularisation + SVC, which identified 1,406 genes during the feature selection stage (ED Table 4), and performed with 0.441 MCC and 0.627 weighted F-measure on the evaluation set. During cross-validation, the mean test MCC score = 0.619 (ED Table 5). ii) XGBoost-based feature selection + SVC, which returned 211 genes, when learning_rate=0.3, boosting trees max_depth=3, and number of rounds n_estimators=100 for feature selection, and a strong (C = 200) SVM classifier (ED Table 4). This algorithm outperformed the simpler logit-SVC model with 0.578 MCC and 0.723 weighted F-measure on the evaluation set (Fig. 2a; ED Fig. 5a; during cross-validation, the mean test MCC score = 0.871; ED Table 5). These models were used to project learned regional differences onto ‘whole’ hypothalamus datasets(Zeisel et al., 2018; Kim et al., 2020; Romanov et al., 2020; Hajdarovic et al., 2022). To do so, we used the original model to select directly from the whole set of available genes from the regional dataset matrices. Thus, our models were re-fitted to a subset of genes available for prediction in a particular dataset, resulting in 4 different models/pipelines. Given these constraints, we have initiated an ad hoc approach to save processing time and to limit interpretation, resulting in a shared model across each predicted dataset. Herein, we i) selected subset genes that were expressed in more than 10 cells in each ‘whole’ hypothalamus dataset, ii) took the intersection of these genes across the 4 reference datasets. This resulted in 2 best-models selected by cross-validation: a) fit strength C = 10 and 1,378 genes (ED Table 4) that were used for the logit-SVC model, which performed with a mean test MCC score of 0.714; b)fit strength C = 200, learning_rate=0.5, boosting trees max_depth=3 and number of rounds n_estimators=100, returning 207 genes for the XGBoost-SVC model (ED Table 4), which performed with a mean test MCC score of 0.770 (ED Table 5). We used these final models to predict subclasses of astrocytes that could be spatially segregated and be present in each ‘whole’ hypothalamus dataset. On our evaluation set, ad hoc version models performed: i) 0.539 MCC and 0.703 weighted F-measure for the logit-SVC model, and 2) 0.518 MCC and 0.686 weighted F-measure for the XGBoost-SVC model. Spatially restricted modalities of astrocytes predicted by these direct versions of AstroTRAP (ED Fig. 4A/Step 13) were histochemically validated and functionally interrogated experimentally in cultured cells and in vivo.
Functional annotation and validation of the regional heterogeneity of astrocytes
Gene regulatory networks (GRN) analysis by SCENIC: We used SCENIC to confirm the GRN-driven heterogeneity amongst astrocytes, at a resolution that distinguished subgroups within distinct hypothalamic areas (Fig. 2B,C; ED Table 6). In doing so, we used normalized expression matrices of astrocyte subsets from ‘whole’ hypothalamus reference datasets, and then applied the SCENIC pipeline(Aibar et al., 2017) (docker://aertslab/pyscenic:0.12.1). to infer GRNs and their activity scores. For the aucell procedure, we have built GRNs only using enriched motifs with a NES of ≥ 3.0. We then took only directly annotated transcription factors (TFs) or TF annotated for an orthologous gene into account and retained GRNs with ≥ 3 genes. We used the aucell matrix to visualize dotplots of GRN activity, and to build a dendrogram by using Ward clustering(Murtagh and Legendre, 2014) (Fig. 2B; ED Fig. 5).
Ligand-receptor expression pairs between astrocytes and neurons: To validate the regional specificity of Lxn+ / Insr+ astrocytes, astrocyte-driven intercellular signalling cascades were selected, and estimated using CellChat(Jin et al., 2021), Liana-py(Dimitrov et al., 2022), and a rank aggregation method (including CellPhoneDB(Efremova et al., 2020), CellChat(Jin et al., 2021), ICELLNET(Noël et al., 2021), connectomeDB2020(Hou et al., 2020), and CellTalkDB(Shao et al., 2021)). To provide a more accurate and transparent estimate of effect sizes and their confidence intervals, the Data Analysis with Bootstrap-coupled ESTimation (DABEST)(Ho et al., 2019) was conducted examining the cumulative effect of cell-to-cell communications between Lxn+ and Lxn- or Insr+ and Insr- astrocytes and neurons in different physiological states. Therefore, the data are cleaned by filtering based on p-values, retaining only results with a significance threshold of less than 0.05, then merged into a single dataset, with additional categorical variables added to denote the region, condition, and control status. We included all ligand-receptor pairs regardless of their presence in each dataset or unique. Splitting the data into multiple groups and performing unpaired comparisons of the magnitude and specificity rankings between groups we produced Gardner-Altman plots of mean difference and Cohen’s d effect size, which provided a clear visualization of the differences between groups(Ho et al., 2019). The analysis results conducted using CellChatDB(Jin et al., 2021) only or all databases pulled by Liana-py(Dimitrov et al., 2022) showed consistent effects, so we preferred using CellChatDB as a manually curated one.
Sample sizes, statistics, and reproducibility for ‘AstroTRAP’: Comparisons of model performance for hyperparameter optimization during grouped 10-fold cross-validation were addressed using GridSearchCV. The performance of models was evaluated using matthews_corrcoef (MCC). The output of GridSearchCV did not provide information on the certainty of the differences between the models. Therefore, we have conducted statistical tests on MCC results. Several variance-corrected statistical tests have been developed to meet these demands. We used the Nadeau and Bengio’s corrected t-test(Nadeau and Bengio, 1999) to obtain the highest replicability scores (which rate how similar the performance of a model is when evaluating it on different random partitions of the same dataset), while maintaining the lowest possible rate of false positives and false negatives. We implemented this analysis under both frequentist and Bayesian statistical frameworks. Herein, testing assumed a corrected right-tailed paired t-test to evaluate if the performance of the 1st model was superior to the 2nd model, where our null hypothesis was that the 2nd model performed at least as good as the 1st model. Moreover, we used Bayesian estimation to calculate the probability that the 1st model is better than the 2nd. Bayesian estimation provides a distribution followed by the mean μ of the difference in the performance of two models. To obtain the posterior distribution, we assumed a priori how the mean might be distributed before working with the actual data and multiplied those by a likelihood function that computes how likely our observed differences were given the values that the mean of differences could take. One way to define posterior distribution is by using a closed-form expression to select a prior conjugate to the likelihood function. Benavoli et al.(Benavoli et al., 2017) showed that, when comparing the performance of two classifiers, one can model the prior as a normal-gamma distribution (with both mean and variance unknown) conjugated to a normal likelihood, thus expressing the posterior as a normal distribution. Marginalizing the variance from this normal posterior, the posterior of the mean parameter can be defined as Student’s t-distribution. Overall, we computed the probability that a model performs better, worse, or quasi-equivalent to another using the Bayesian approach.
DGE of the assumed regionalized subclasses of astrocytes projected onto ‘whole’ hypothalamus datasets and UMIs counts matrices were statistically tested to obtain DEGs for n = 8 available hypothalamic territories using the MAST test(Finak et al., 2015). We used the Logreg test(Ntranos et al., 2019) to define DGE across cells tentatively assigned to projected hypothalamic areas on ‘whole’ hypothalamus reference datasets. Finally, we applied rank aggregation of the DEGs across the different datasets to derive individual lists of markers for each nucleus(Kolde et al., 2012). We targeted RRA on the p-value to determine the specificity score and on the log2 fold change to determine the magnitude score (ED Fig. 4A/Step 14; Fig. 2A-F; ED Table 6).
Other packages: Visualizations and figures were created in the ggplot2 (v3.4.2), cowplot (v1.1.1)(Wilke, 2020), patchwork (v1.1.2.9000), and scCustomize (v1.1.1) packages using the viridis color palettes (v0.6.2) for continuous data. UpSet plots(Conway, Lex and Gehlenborg, 2017) were produced using the UpSetR package (v1.4.0)(Gehlenborg, 2019) with help from the gridExtra package (v2.3)(Auguie, 2017). Data manipulation was performed using tidyverse (v2.0.0.9000), particularly dplyr (v1.1.2), tidyr (v1.1.2), and ork (v1.0.1)(Wickham et al., 2019). Analysis was managed in the Snakemake system (v7.21.0)(Mölder et al., 2021), and orkflow (v1.7.0)(Blischak, Carbonetto and Stephens, 2019), which was also used to produce the publicly available website for the analysis code, results, and output. Reproducible reports were produced using Quarto (v1.2), knitr (1.42)(Xie, 2014) and R Markdown (v2.21)(Xie, Allaire and Grolemund, 2018), and converted to HTML using Pandoc (v2.19.2).
Ethical approval of animal studies: Experiments on live animals conformed to the 2010/63/EU European Communities Council Directive and regulated by applicable local laws (Tierversuchsgesetz 2012, BGBI, Nr. 114/2012, Austria). Experimental protocols were approved by the Austrian Ministry of Science and Research (66.009/0145-WF/II/3b/2014, and 66.009/0277-WF/V/3b/2017). Feeding experiments using special diets were approved by the Institutional Animal Care and Use Committee of Yale University and performed in its Animal Resources Center. Particular effort was directed towards minimizing the number of animals used, and their suffering during experiments.
Mouse strains: Mice were group housed (n = 3 – 5) at 22 C° – 24 C° using a 12-h light/12-h dark cycle. Animals had ad libitum access to water and specified diet at all times. Postnatal animals were weaned on postnatal day (P)21. Mouse strains were commercially available, as follows: C57Bl/6J (‘wild-type’, RRID:IMSR_JAX:000664), Ai6 (RRID:IMSR_JAX:007906), Ai14 (RRID:IMSR_JAX:007914), Pomc–gfp (RRID:IMSR_JAX:009593), Mfn2f/f (RRID:IMSR_JAX:026525), TRAP1 (RRID:IMSR_JAX:021882), TRAP2 (RRID:IMSR_JAX:030323). Experiments were not randomized, neither were the investigators blinded to the experimental variables and assignments.
Stress experiments in TRAP mice: Experiments were performed as previously described(Alpár et al., 2018). Briefly, tamoxifen injection (150 mg/kg, i.p.) in TRAP1 mice(Alpár et al., 2018) was followed by the injection of 4% PFA (in 50 μl physiological saline) in the right hindpaw 24h later. After a lag-time of 72 h to allow for ZsGreen expression in ‘stress-responder’ cells, mice were transcardially perfused as below.
Circadian activity of astrocytes in vivo: TRAP2 mice(DeNardo et al., 2019) crossed with Ai14 reporter mice were injected with 4-OH-tamoxifen, which allowed for tissue collection in a narrow time window to assess Fos expression. 4-OH-tamoxifen was injected during the light (CT6) and dark (CT18) periods, with tissues collected 6h later.
Tissue collection and fixation: Animals were transcardially perfused with 50–100 ml of a fixative composed of 4% paraformaldehyde (PFA) in 0.1M phosphate buffer (PB; pH 7.4), the brains dissected out, and post-fixed in the same fixative overnight. Alternatively, animals were deeply anesthetized (5% isoflurane (AbbVie) in 1L/min air flow), their brains rapidly dissected, and immersion-fixed in 4% PFA in phosphate-buffered saline (PBS, 0.05M, pH 7.4) at 4 °C for 24 h. Tissues were then extensively washed in PB, cryoprotected by immersion in 30% sucrose in distilled water at 4 °C 24–48h, and processed as described below.
Tissue preparation and histochemistry: Free-floating sections were cut in 1-in-6 series at 50 μm thickness on a cryostat microtome. Glass-mounted serial sections had a thickness of 16 μm. After rinsing in 0.1M PB, specimens were exposed to a blocking solution composed of 0.1M PB, 10% normal donkey serum (NDS), 5% BSA, and 0.3% TX-100 for 3h. This was followed by incubation (for 48h) with select combinations of primary antibodies: goat anti-GFP (1:1,000; Abcam, #ab6662, RRID:AB_305635: lot GR311622-15, GR311622-7), chicken anti-GFP (1:500, Aves Labs, #GFP-1020, RRID:AB_10000240: lot GFP697986), chicken anti-mCherry (1:1,000; EnCor Biotechnology, #CPCA-mCherry, RRID:AB_2572308: lot 7670-4), guinea pig anti-GFAP (1:500; Synaptic Systems, #173 004, RRID:AB_10641162: lot 2-15, 2-17), rabbit anti-LXN (1:400; Sigma-Aldrich, #HPA014179, RRID:AB_1853404), rabbit anti-S100b (1:4,000; Synaptic Systems, #287 003, RRID:AB_2620024), mouse anti-NEUN (1:1,000; Millipore, #MAB377, RRID:AB_2298772), rabbit anti-AQP4 (1:4,000; Sigma-Aldrich, #A5971, RRID:AB_258270). After rinsing, tissue sections were exposed to cocktails of secondary antibodies (all from Jackson ImmunoResearch), including Alexa Fluor 488-AffiniPure donkey anti-goat (705-545-147, lot 131669), Alexa Fluor 488 donkey anti-mouse (715-545-151, lot 127820), Alexa Fluor 488-AffiniPure donkey anti-guinea pig (706-545-148, lot 138058), Alexa Fluor 647-AffiniPure donkey anti-guinea pig (706-605-148, lot 135631), Alexa Fluor 647-AffiniPure donkey anti-rabbit (711-605-152, lot 127614), carbocyanine (Cy)2-AffiniPure donkey anti-rabbit (711-225-152, lot 139999), Cy3-AffiniPure donkey anti-chicken (703-165-155, lot 142225), Cy3-AffiniPure donkey anti-goat (705-165-147, lot 134527), Cy3-AffiniPure donkey anti-guinea pig (706-165-148, lot 134844), Cy3-AffiniPure donkey anti-mouse (715-165-150, lot 116881), and Cy3-AffiniPure donkey anti-rabbit (711-165-152, lot 141941), that were applied at a dilution of 1:300 in 0.1 MPB supplemented with 2% BSA (20-22 °C, 2h). Nuclei were routinely counterstained with Hoechst 33,342 (1:10,000; Sigma). Tissues were coverslipped with entellan in toluene (Merck). Images were acquired in the ZEN2010 software package on a Zeiss LSM880 laser-scanning microscope equipped with appropriate excitation and emission filters for optimal signal separation.
Fluorescent in situ hybridization (HCR 3.0): Multicolor stainings were performed on fresh-frozen tissues sectioned at 16 μm following the HCR v3.0 protocol for ‘generic sample on the slide’ (Molecular Instruments). Pre-treatment of tissue sections included, sequentially, fixation with 4% PFA for 15 min, 2x washing steps with PBS, and dehydration in an ascending EtOH gradient (25%, 50%, 75%, and 100%, each step for 5 min with subsequent drying for 15 min). Tissues were obtained from 8-12 weeks-old wild-type mice. Hybridization probes (Apoe: NM_009696.4; Gfap: NM_001131020; Slit2: NM_001291227.2; Aldh1a1: NM_001361503.1; Tafa1: NM_182808.3; Plcb1: NM_001145830.1; Sgcd: NM_011891.5; Slc38a1: NM_001166456.1; Fos: NM_010234.3; Gja1: NM_010288.3; Snap25: NM_011428.3; Olig1: NM_016968.4; Htra1: NM_019564.3; Lxn: NM_016753; Adcyap1r1: NM_007407.4; Npy1r: NM_010934.4; Trhr: NM_013696.2; Tacr1: NM_009313.5; Grpr: NM_008177.3; Hcrtr2: NM_001364551.1; Prokr2: NM_144944.3; Otp: NM_011021.5; Isl1: NM_021459.4; Tbx3: CCDS 19614.1; Nr4a1: CCDS 27848.1; Foxg1: NM_005249.5; Pitx2: NM_001042504.2) were purchased from Molecular Instruments.
Preparation, purification, and pharmacological probing of primary astrocytes: Astrocytes were isolated from P6-P7 wild-type mice, which were anesthetized with 5% isoflurane (AbbVie, 1L/min flow rate), decapitated, their brains rapidly extracted and pooled in ice-cold Hanks′ Balanced Salt Solution (without Ca2+, Mg2+; Thermo Fisher). The meninges were peeled away with a fine forceps (Dumont No. 5; FST), and the hypothalamus was isolated by a curved-blade surgical scalpel. Previous protocols(McCarthy and de Vellis, 1980) were then adapted to prepare high-purity cultures. Tissues were transferred into astrocyte growth medium, composed of Dulbecco’s Modified Eagle Medium (DMEM, high glucose, GlutaMAX™ supplement; Thermo Fisher), 10% fetal bovine serum (FBS, Gibco), and 1% penicillin/streptomycin (100 U/ml; Thermo Fisher), triturated, and centrifuged at 1,100 rpm for 5 min. After discarding the supernatant, tissues were digested in DMEM (with high glucose and GlutaMAX™; Thermo Fisher), 0.4% trypsin (Thermo Fisher) and 5% DNAse (Thermo Fisher) at 37 oC for 5 min. After terminating the reaction with 2% BSA, tissues were triturated with a serological pipette 3-4 times. Single cell suspension was generated by repeated washes, trituration, and 3 rounds of centrifugation (1,100 rpm, 3 min, at 22-24 oC). The resulting suspension was passed through a cell strainer (40 µm pore size, Fischer Scientific), washed, centrifuged, resuspended, and plated in astrocyte growth medium in T-75 flasks (106 cells; VWR). Cells were cultured in astrocyte growth medium at in 5% CO2 atmosphere at 37 °C. Medium was partially (1/3) replaced every other day. At ≥90% confluency, flasks were placed on an orbital shaker at 210 rpm at 37 °C overnight, thus separating neurons, microglia, and oligodendrocytes from adherent astrocytes. After discarding the supernatant, astrocytes were rinsed with Hanks’ Balanced Salt Solution (HBSS (+); with Ca2+, Mg2+; Thermo Fisher), and detached from the flasks’ surface with dissociation medium (0.25% trypsin; Thermo Fisher). Purified astrocytes were subcultured for 2-7 days, and then seeded (either for immunocytochemistry at 5 x 104 cells/well in poly-D-lysine (Merck)-coated 24-well plates (VWR) containing 12-mm round coverslips (Carl Roth) or 3 x 105 cells/well in 6-well plates (VWR) for biochemistry), and used for experiments. Cell pellets and supernatants were simultaneously processed when needed.
The purity of astrocytes was controlled by immunocytochemistry. In brief, cells were washed 3x with ice-cold PBS (pH 7.4, Life Technologies) and fixed with 4% PFA (in 0.05M PBS; Sigma) at 22-24 oC for 10-15 min, and permeabilized by using 0.2% Triton X-100 (Sigma), 10% normal donkey serum (NDS; Jackson ImmunoResearch), 5% BSA (Sigma) in 0.05M PBS) at 22-24 oC for 60 min. After rinsing, cells were stained by a mixture of antibodies, including rabbit anti-ALDH1L1 (1:4,000; Synaptic Systems, #278 102, RRID:AB_2884922; for astrocytes), guinea pig anti-GFAP (1:500; Synaptic Systems, #173 004, RRID:AB_10641162: lot 2-15, 2-17; for astrocytes), goat anti-IBA1 (1:500; Abcam, #ab289874, RRID:AB_2942069; for microglia), rabbit anti-OLIG2 (1:200; Millipore, #AB9610, RRID:AB_570666; for oligodendrocytes), mouse anti-NeuN (1:1,000; Millipore, #MAB377, RRID:AB_2298772; for neurons) diluted in 0.2% Triton X-100, 5% NDS, 2% BSA in 1X PBS at 4°C overnight. Controls were carried out under identical conditions except without primary antibodies. After repeated rinses in 0.05M PBS, the specimens were exposed to species-specific secondary antibodies (Alexa Fluor 488- or carbocyanine (Cy)2/3/5-tagged (all 1:300; Jackson ImmunoResearch) diluted in 0.05M PBS, 2% BSA and Hoechst 33,342 (1:10,000 (Sigma); nuclear counterstain) at 22-24 oC for 2h. After another was in 0.05M PBS, coverslips were dipped in distilled water, mounted with glycerol (30 µL/coverslip), and then inspected by using a Zeiss LSM880 laser-scanning microscope. Emission spectra for each dye were limited as follows: Cy2 (505–530 nm), Cy3 (560–610 nm), and Cy5 (650–720 nm). Images were acquired in the ZEN2010 software package. Each coverslip was tested at 5 independent (random) fields of view, with the means ± s.d. of the cell counts from at least 4 cultures processed to establish a ‘purity index’ (that is the percentage of GFAP+ or ALDH1L1+ cells/total cells). Cultures were only processed with purity ≥95%.
Cell viability assay: The viability of cells, plated at a density of 25,000 cells/well in 24-well plates (VWR), was tested using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay(Mosmann, 1983). In brief, growth media was replaced with 0.25 mL of MTT tetrazolium solution (1 mg/mL MTT tetrazolium (Abcam)), and exposed for 30 min at 37 °C. Subsequently, the incubation medium was replaced by 0.25 mL dimethyl-sulfoxide (Sigma), and the absorbance of the lysates was measured spectrophotometrically at 570 nm. Data in this report are from n = 8 independent observations (ED Fig. 14).
Latexin release from astrocytes: Confluent cultures of astrocytes (in 6-plate format) were starved in serum-free growth medium overnight. Subsequently, the medium was replaced with either HBSS (-), ATP (100 µM; Sigma), or HBSS (+) for 5 min. To determine if insulin alters latexin release, cultured astrocytes were exposed to 0.05 µM, 0.1 µM, 0.3 µM, or 1 µM insulin (Sigma) that had been dissolved in serum-free growth medium for 30 min. Experiments were performed in triplicate. Subsequently, cell lysates, as well as protein fractions of the media were probed for latexin by Western blotting.
Quantitative Western blotting with total protein normalization: Samples (cell lysate, medium, and cell fractions) were obtained as described above. Western blot analysis was carried out using an Amersham WB System (GE Healthcare, Little Chalfont, UK). Samples were labeled with carbocyanine (Cy)5 dye reagent (GE Healthcare; prediluted in ultrapure water (1:10); for total protein staining), reduced and boiled (at 95°C for 3 min) in sample buffer prior to loading onto Amersham WB 13.5% gel cards (GE Healthcare, Little Chalfont, UK). Electrophoresis (600 V, 42 min) and protein transfer onto polyvinylidene-difluoride (PVDF) membranes (100 V, 30 min) were performed according to the manufacturer’s instructions. After 1h of blocking in 3% BSA (Sigma) in standard Tris-buffered saline (TBS), membranes were incubated overnight at 4°C in primary antibody solutions against LXN (1:200: goat, ThermoFisher Scientific, #PA5-18676, RRID:AB_10980158). Antibody binding was detected by using species-specific (anti-goat) Amersham WB Cy3 secondary antibody (1:1000 in 3% BSA in TBS; Amersham, GE Healthcare) for 1h at RT. Following several washing steps in standard TBS-Tween20 (TBST) and finally in TBS, membranes were dried before scanning at auto excitation. Automated image analysis was performed with the Amersham WB evaluation software (v1.0, GE Healthcare, Little Chalfont, UK) with manual optimization if necessary. Target-specific immunoreactivities were normalized to total protein intensities and statistically analyzed.
Diet-induced obesity: Control mice were fed a regular chow diet containing 57% of calories as carbohydrates, 34% of calories as protein, and 9% calories as a mixture of fatty acids. In contrast, a high-fat diet (HFD) contains 35% of its calories as carbohydrates, 20% calories as protein, and 45% calories as fatty acids (Research Diets). All experiments were performed in adult male mice during the period of P42-P170. Food intake and body weight were assessed weekly. Representative food intake shown was measured on the last week of HFD exposure.
Stereotaxic injection, viral particles: Bilateral virus injections were made into the ARC of anesthetized 6/7-week-old male Mfn2f/f mice placed into a stereotaxic apparatus (model 902; David Kopf instruments). AAV8-Gfap-Gfp (for control mice) or AAV8-Gfap-Gfp-Cre (Virus Vector Core, UNC; to generate astrocyte-specific knock-down) were applied at a volume of 300nl/hemisphere at coordinates (from bregma): anterior–posterior: −1.2 mm, lateral: ±0.3 mm, dorsal–ventral: −5.8 mm by using an air pressure system (injection time: 5 min). After surgery, mice were allowed to recover for 10 days before any dietary manipulation. Accurate virus injection into the ARC was verified by analyzing local GFP fluorescence. Mice with ‘missed’ or ‘partial’ hits were excluded. Specific AAV expression in ARC was also signified by double fluorescence labeling for GFP and GFAP.
Body composition: Lean and fat mass were analyzed with EchoMRI (EchoMRI LLC)(Varela, Kim, et al., 2021).
Electron microscopy: Mice (n ≥ 4 per group) were anesthetized and transcardially perfused with freshly prepared 4% PFA and 0.1% glutaraldehyde as reported(Varela, Kim, et al., 2021; Varela, Stutz, et al., 2021). After post-fixation overnight, vibratome sections (50 μm) containing the ARC were immunostained with rabbit anti-POMC (1:7,500, H-029-30, Phoenix Pharmaceuticals,) or mouse anti-GFAP (1:4,500, Sigma) primary antibodies. After overnight incubation at 22-24 oC, sections were washed with 0.1M phosphate buffer (PB; pH7.4), incubated with either biotin-conjugated donkey anti-rabbit or donkey anti-mouse IgG secondary antibodies (1:250, Jackson ImmunoResearch) for 2h, washed again, placed in pre-formed avidin–biotin complex (Vector Laboratories), and developed with 3,3-diaminobenzidine and 0.01% H2O2. Sections were then osmicated (1% OsO4 for 15 min), and dehydrated in an ascending series of ethanol. During dehydration, 1% uranyl acetate was added to the 70% ethanol step to enhance ultrastructural membrane contrast. Flat embedding in Durcupan followed dehydration. Ultrathin sections were cut on a Leica ultra-microtome, collected on Formvar-coated single-slot grids, and analyzed with a Tecnai 12 Biotwin electron microscope (FEI) with an AMT XR-16 camera(Varela et al., 2017; Varela, Stutz, et al., 2021).
Quantification of mitochondria, glial coverage, and synaptic input: Hypothalamic sections containing either POMC+ or GFAP+ cells with a visible nucleus were analyzed by electron microscopy. Mitochondrial cross-sectional area was calculated. For glia coverage and synaptic inputs, a blinded investigator scored the percentage of glia and the number of synapses per POMC+ neuron in high-magnification images (>4.800x)(Varela, Kim, et al., 2021; Varela, Stutz, et al., 2021).
Statistics: Experimental data were analyzed using GraphPad Prism 8.0.2 (GraphPad). Changes in Fos expression in the SCN (circadian activity and TRAP2 mice at CT6, CT18; n = 3 male mice per condition, P = 1) and PVN (TRAP mice after acute stress; n = 7 male mice per condition, P = 0,017) were analyzed across neurons and/or astrocytes (multiple groups, as applicable) with two-tailed t-test with Welch’s correction. Morphological parameters of astrocytes and synapses within ARC, as well as phenotypic (metabolic) parameters in the Mfn2f/f mouse model under control conditions and HFD-induced obesity, were compared using two-tailed Student’s t-test; the sample size was at least 4 animals per condition; experiments were performed in adult male mice at the age of 6-25 weeks. Data were expressed as means ± s.e.m. throughout, except in box-and-whisker plots that show medians ± s.e.m. Statistical significance was defined as *p < 0.05; **p < 0.01; ***p < 0.001. Statistical comparisons of classification models performance(Nadeau and Bengio, 1999; Benavoli et al., 2017), DABEST multiple groups comparisons of the magnitude and specificity rankings of ligand-receptor pairs(Ho et al., 2019; Jin et al., 2021; Dimitrov et al., 2022), and differential genes expression testing(Finak et al., 2015; Ntranos et al., 2019) were described in corresponded sections above.
All scRNA-seq datasets had been deposited in GEO previously and reanalysed here (accession numbers and metadata are available in ED Table 1). All data presented (for example, imaging) will be made available by T. Harkany (tibor.harkany@ki.se or tibor.harkany@meduniwien.ac.at) upon reasonable request.
The code used will be available from figshare.
versions <- purrr::map(versions, as.character)
versions <- jsonlite::toJSON(versions, pretty = TRUE)
readr::write_lines(
versions,
here::here("output", DOCNAME, "package-versions.json")
)
knitr::kable(data.frame(
File = c(
get_download_link("package-versions.json", here::here("output", DOCNAME))
),
Description = c(
"Packages and their versions used for this analysis"
)
))| File | Description |
|---|---|
| package-versions.json | Packages and their versions used for this analysis |
Auguie, B. (2017) “gridExtra: Miscellaneous Functions for ‘Grid’ Graphics.”
Wilke, C.O. (2020) “cowplot: Streamlined Plot Theme and Plot Annotations for ggplot2.”
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0
[4] dplyr_1.1.3 purrr_1.0.2 readr_2.1.4
[7] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4.9000
[10] tidyverse_2.0.0.9000 jsonlite_1.8.7 reticulate_1.34.0
[13] knitr_1.44 glue_1.6.2 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.4 xfun_0.40 bslib_0.5.1 processx_3.8.2
[5] lattice_0.22-4 callr_3.7.3 tzdb_0.4.0 vctrs_0.6.4
[9] tools_4.3.1 ps_1.7.5 generics_0.1.3 parallel_4.3.1
[13] fansi_1.0.5 pkgconfig_2.0.3 Matrix_1.6-1.1 lifecycle_1.0.3
[17] compiler_4.3.1 git2r_0.32.0 munsell_0.5.0 getPass_0.2-2
[21] httpuv_1.6.12 htmltools_0.5.6.1 sass_0.4.7 yaml_2.3.7
[25] later_1.3.1 pillar_1.9.0 crayon_1.5.2 jquerylib_0.1.4
[29] whisker_0.4.1 cachem_1.0.8 tidyselect_1.2.0 digest_0.6.33
[33] stringi_1.7.12 rprojroot_2.0.3 fastmap_1.1.1 grid_4.3.1
[37] here_1.0.1 colorspace_2.1-0 cli_3.6.1 magrittr_2.0.3
[41] utf8_1.2.4 withr_2.5.1 scales_1.2.1 promises_1.2.1
[45] bit64_4.0.5 timechange_0.2.0 rmarkdown_2.25 httr_1.4.7
[49] bit_4.0.5 png_0.1-8 hms_1.1.3 evaluate_0.22
[53] rlang_1.1.1 Rcpp_1.0.11 rstudioapi_0.15.0 vroom_1.6.4
[57] R6_2.5.1 fs_1.6.3