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MB Sample ID: SA185403

Local Sample ID:	human CSF 20
Subject ID:	SU002057
Subject Type:	Mammal
Subject Species:	Homo sapiens
Taxonomy ID:	9606
Gender:	Male and female

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Sample Preparation:

Sampleprep ID:	SP002063
Sampleprep Summary:	For characterization by mass spectrometry, CSF was acquired 4h, 24h, and 48h following a single 75 mg/kg MTX injection from 6-8 mice and flash frozen for further analysis. Per condition, 3 μl of CSF were extracted by brief sonication in 240 μl 100% methanol, supplemented with isotopically labeled internal standards (17 amino acids and reduced glutathione, Cambridge Isotope Laboratories, MSK-A2-1.2 and CNLM-6245-10) and 60 μl 20 mM Ellman’s reagent in water (Sigma-Aldrich, D8130). After centrifugation for 10min. at maximum speed on a benchtop centrifuge (Eppendorf) the cleared supernatant was dried using a nitrogen dryer and reconstituted in 30 µl water by brief sonication. Extracted metabolites were spun again and cleared supernatant was transferred in LC-MS microvials. A small amount of each sample was pooled and serially diluted 3 and 10-fold to be used as quality controls throughout the batch run. Two microliters (equivalent to 0.2 ul of CSF) of reconstituted sample were injected into a ZIC-pHILIC 150 × 2.1 mm (5 µm particle size) column (EMD Millipore) operated on a Dionex UltiMate 3000 UPLC system (Thermo Fisher Scientific). Chromatographic separation was achieved using the following conditions: buffer A was acetonitrile; buffer B was 20 mM ammonium carbonate, 0.1% ammonium hydroxide. Gradient conditions were: linear gradient from 20% to 80% B; 20–20.5 min: from 80% to 20% B; 20.5–28 min: hold at 20% B. The column oven and autosampler tray were held at 25 °C and 4 °C, respectively. The MS data acquisition was on a QExactive benchtop orbitrap mass spectrometer equipped with an Ion Max source and a HESI II probe and was performed in a range of m/z= 70–1000, with the resolution set at 70,000, the AGC target at 1x106, and the maximum injection time (Max IT) at 20 msec. For tSIM scans, the resolution was set at 70,000, the AGC target was 1x105, and the max IT was 100 msec. Relative quantitation of polar metabolites was performed with TraceFinder 4.1 (Thermo Fisher Scientific) using a 5 ppm mass tolerance and referencing an in-house library of chemical standards. Pooled samples and fractional dilutions were prepared as quality controls and only those metabolites were taken for further analysis, for which the correlation between the dilution factor and the peak area was >0.95 (high confidence metabolites). Normalization for biological material amounts was based on the total integrated peak area values of high-confidence metabolites within an experimental batch after normalizing to the averaged factor from all mean-centered chromatographic peak areas of isotopically labeled amino acids internal standards (Cambridge Isotope Laboratories). The data were Log transformed and Pareto scaled for MetaboAnalyst-based statistical or pathway analysis (41). We profiled 200 metabolites, 85 of which were detected in CSF and passed our quality control protocol. Single-cell transcriptomics Mouse embryonic ChP single cell RNA-seq dataset was acquired and analyzed in (35). Briefly, whole embryonic ChP tissue from each ventricle was micro-dissected, digested, and live cells were FACS sorted. Single cells (~7,000 cells) were processed through the 10X Genomics Single Cell 3’ platform. Variable genes were selected by using the method described in (42). Briefly, a logistic regression model was fit to the cellular detection fraction as a function of total numbers of unique molecular identifiers (UMIs) per cell. Outliers from this curve (i.e., genes expressed in fewer cells than expected given the number of UMIs) were genes that contained more variance than proportionally expected, and therefore were particularly suited for reducing the dimensionality of the dataset. We used a threshold of deviance of -0.15 and determined the variable genes independently for each of three experimental replicates. The genes that were at the intersection of these three replicate gene lists (i.e., global variable genes) were used for downstream analysis. The expression matrix was restricted to the subset of these global variable genes, and then centered and scaled before performing principle component analysis (PCA) using Seurat’s RunPCA() function. After PCA, the number of significant principle components was determined to be 5 using the elbow method. The data were then visualized as a 2D embedding of the significant principle components using t-SNE (43) via the RunTSNE() function in Seurat.

Sampleprep ID:

SP002063

Sampleprep Summary:

For characterization by mass spectrometry, CSF was acquired 4h, 24h, and 48h following a single 75 mg/kg MTX injection from 6-8 mice and flash frozen for further analysis. Per condition, 3 μl of CSF were extracted by brief sonication in 240 μl 100% methanol, supplemented with isotopically labeled internal standards (17 amino acids and reduced glutathione, Cambridge Isotope Laboratories, MSK-A2-1.2 and CNLM-6245-10) and 60 μl 20 mM Ellman’s reagent in water (Sigma-Aldrich, D8130). After centrifugation for 10min. at maximum speed on a benchtop centrifuge (Eppendorf) the cleared supernatant was dried using a nitrogen dryer and reconstituted in 30 µl water by brief sonication. Extracted metabolites were spun again and cleared supernatant was transferred in LC-MS microvials. A small amount of each sample was pooled and serially diluted 3 and 10-fold to be used as quality controls throughout the batch run. Two microliters (equivalent to 0.2 ul of CSF) of reconstituted sample were injected into a ZIC-pHILIC 150 × 2.1 mm (5 µm particle size) column (EMD Millipore) operated on a Dionex UltiMate 3000 UPLC system (Thermo Fisher Scientific). Chromatographic separation was achieved using the following conditions: buffer A was acetonitrile; buffer B was 20 mM ammonium carbonate, 0.1% ammonium hydroxide. Gradient conditions were: linear gradient from 20% to 80% B; 20–20.5 min: from 80% to 20% B; 20.5–28 min: hold at 20% B. The column oven and autosampler tray were held at 25 °C and 4 °C, respectively. The MS data acquisition was on a QExactive benchtop orbitrap mass spectrometer equipped with an Ion Max source and a HESI II probe and was performed in a range of m/z= 70–1000, with the resolution set at 70,000, the AGC target at 1x106, and the maximum injection time (Max IT) at 20 msec. For tSIM scans, the resolution was set at 70,000, the AGC target was 1x105, and the max IT was 100 msec. Relative quantitation of polar metabolites was performed with TraceFinder 4.1 (Thermo Fisher Scientific) using a 5 ppm mass tolerance and referencing an in-house library of chemical standards. Pooled samples and fractional dilutions were prepared as quality controls and only those metabolites were taken for further analysis, for which the correlation between the dilution factor and the peak area was >0.95 (high confidence metabolites). Normalization for biological material amounts was based on the total integrated peak area values of high-confidence metabolites within an experimental batch after normalizing to the averaged factor from all mean-centered chromatographic peak areas of isotopically labeled amino acids internal standards (Cambridge Isotope Laboratories). The data were Log transformed and Pareto scaled for MetaboAnalyst-based statistical or pathway analysis (41). We profiled 200 metabolites, 85 of which were detected in CSF and passed our quality control protocol. Single-cell transcriptomics Mouse embryonic ChP single cell RNA-seq dataset was acquired and analyzed in (35). Briefly, whole embryonic ChP tissue from each ventricle was micro-dissected, digested, and live cells were FACS sorted. Single cells (~7,000 cells) were processed through the 10X Genomics Single Cell 3’ platform. Variable genes were selected by using the method described in (42). Briefly, a logistic regression model was fit to the cellular detection fraction as a function of total numbers of unique molecular identifiers (UMIs) per cell. Outliers from this curve (i.e., genes expressed in fewer cells than expected given the number of UMIs) were genes that contained more variance than proportionally expected, and therefore were particularly suited for reducing the dimensionality of the dataset. We used a threshold of deviance of -0.15 and determined the variable genes independently for each of three experimental replicates. The genes that were at the intersection of these three replicate gene lists (i.e., global variable genes) were used for downstream analysis. The expression matrix was restricted to the subset of these global variable genes, and then centered and scaled before performing principle component analysis (PCA) using Seurat’s RunPCA() function. After PCA, the number of significant principle components was determined to be 5 using the elbow method. The data were then visualized as a 2D embedding of the significant principle components using t-SNE (43) via the RunTSNE() function in Seurat.