dataesr/scientific-paragraphs-categorization huggingface dataset:
All in all, the dataset-specific dysregulation D score identified midbrain genes with the best combination of significance (low P-value) and expression change (high fold-change) (Fig. 2D, E ). Among the top dysregulated genes in each dataset (highest D dataset scores), we identified a substantial number of genes with functions related to neuronal function, morphology and survival (Fig. 2G ). A subset of these genes has been previously implicated in PD [e. g. , SNCA 9,51 , CHCHD2 6,52 , GAD1 53 , SLC17A6/VGLUT2 54 ]. However, despite two common genes within the top 15 genes list across datasets (MTRNRL1 and ZFP36L2), and some overlap in gene families and associated cellular functions, most of the top 15 genes appeared unique to each dataset. Therefore, we next aimed to identify the most common molecular profile across all these datasets.
Integrated scoring of differential mRNA expression across independent transcriptomics datasets The genes consistently dysregulated across multiple independent transcriptomic datasets may be particularly implicated in initiating PD pathology and overt symptoms. Therefore, to identify such a molecular phenotype, we developed a statistical analysis framework that determines a single overall dysregulation score (D overall ) per gene across multiple transcriptomic datasets generated by different researchers, methods, and conditions (Fig. 2C ). D overall is calculated from the gene dysregulation scores from all six datasets (D datasets ) and adjusted for the degree of concordance in the foldchange direction to prioritize the genes changing expression in the same direction (up-or down-regulated) across datasets (see Methods for details).
Genes consistently dysregulated in PD patients across independent datasets We rank-ordered the entire list of genes (n = 24,693 unique genes expressed in ≥1 dataset) by decreasing D overall score. Among the 20 genes with the highest D overall score, 13 were significantly dysregulated in five out of six independent datasets and HES1 and NAP1L2 ranked the highest (Fig. 3A ). HES1 plays an important role in regulating the location and density of mesencephalic dopaminergic neurons  55. NAP1L2 appears important for regulating histone acetylation during neuronal differentiation  56. NAP1L2 expression was also found to be down-regulated in adult postmortem brain tissue from a large multi-cohort of patients (n = 1,270) with a range of neurodegenerative diseases  57  , suggesting a broad role for NAP1L2 in neurodegeneration.
Gene Ontology (GO) analysis of the top 200 highest-ranked genes revealed significant enrichment for biological processes related to neuronal differentiation and synaptic transmission, and several PD-related biological processes, including dopamine secretion, locomotion, cellular stress response and proteostasis (Fig. 3B  and Supplementary Table  4 ). The endoplasmic reticulum (ER), extracellular matrix and (distal) axon were identified as the major enriched cellular components (Fig. 3C ), all of which have well-established roles in the pathogenesis of PD as well as several other neurodegenerative disorders  [58] [59] [60] [61] [62] [63] [64] [65] [66]. Overrepresentation analysis for gene-chemical interactions from the Comparative Toxicogenomics Database (CTD)  67  uncovered significant associations between our cross-dataset integrated molecular signature and a variety of environmental toxins (i.
e. , pesticides) and ER stress inducers (Fig. 3D  and Supplementary Table  5 ). Exposure to pesticides, most notably rotenone, has been shown to trigger selective degeneration of dopaminergic neurons in the substantia nigra and is a well-recognized risk factor for the onset and development of PD  [68] [69] [70] [71] [72]. Fig. 2  Identification, scoring and ranking of genes dysregulated in Parkinson's disease reprogrammed neurons. A Principal component analysis (PCA) of the transcriptome data from PD patient-and control-reprogrammed neurons collected across six datasets, post-mortem adult human substantia nigra samples (GTEx), and highly pure populations of human floor plate-derived midbrain dopaminergic neurons (iCell® DopaNeurons). In vitro-engineered neuronal tissue clusters separately from post-mortem substantia nigra tissue (GTEx) and groups by method of derivation (iPSC reprogramming or direct iN conversion) irrespective of the laboratory of origin.
Each data point represents a bulk or artificial bulk (for single-cell RNA-seq datasets) tissue transcriptome. Artificial bulk samples were generated by summing up the gene counts from all cells of the same subject. Color indicates dataset of origin (annotation shown in (B)). B Heatmap clustering of the average transcriptomes of the six reprogrammed neuron datasets, GTEx substantia nigra and striatum tissues, and iCell® DopaNeurons using all expressed genes (≥1 transcript per million [TPM] across all samples). Hierarchical clustering is based on average linkage and Euclidean distance-based similarity. The darker shade denotes higher similarity. C Pipeline for computation of a per-gene dysregulation score (D) based on individual-dataset differential expression (DE) analysis results. DE analysis was performed on each dataset independently on both read counts and TPM values, and results were combined using logitp method (for a combination of P-values) and arithmetic mean averaging (for a combination of log2 fold changes).
Combined P-value and log2 fold change measures were mapped to a continuous (0. 01-1) scale using desirability functions  48  , and integrated by weighted geometric averaging to obtain an overall dysregulation score (D) for each gene. Information about whether the gene was expressed in the adult human midbrain was used as a soft filter at 1% of the total weight to prioritize the ranking of relevant genes. Dysregulation scores are integrated across multiple datasets, weighting for cross-dataset similarity in log2 fold change directionality, to obtain an overall dysregulation score per gene (D overall ; refer to Methods for details). Disease phenotype and subject ID are annotated horizontally, and gene function is annotated vertically according to the legend. A subset of individuals (N = 17) was selected to generate both IPSC and iN models, and three neuronal lines were profiled using both single-cell and bulk RNA-sequencing (see Supplementary Table  1  for subject details).
001) but not for schizophrenia (FWER P-value = 0. 240, Kolmogorov-Smirnov test) (Fig. 3E ). Schizophrenia is a neuropsychiatric disorder that,   4  and 5. E Gene Set Enrichment Analysis (GSEA) of multiple-dataset PD dysregulated genes for genes associated with the terms "Parkinsonism", "Dementia" and "Schizophrenia" from the Human Phenotype Ontology (HPO) database  131. "Parkinsonism"and "Dementia"-related genes were significantly overrepresented at the top of the list of expressed genes (n = 24,693) rankordered by decreasing D overall score. F GSEA shows a significant enrichment of PD-symptom-related HPO terms "Bradykinesia", "Rigidity", "Akinesia", "Dyskinesia" and "Postural instability" among the multiple-dataset PD dysregulated genes. Vertical bars in (E, F) represent the "gene hits", i.
Alphasynuclein (SNCA) was the top gene candidate in our integrated analysis of reprogrammed neurons with the highest contribution to parkinsonism-related enrichment. Synaptojanin 1 (SYNJ1) and phosphodiesterase 10 A (PDE10A) were the second and third top enriched genes, both of which play an essential role in the regulation of neuronal synaptic function in the context of PD  [78] [79] [80] [81]. Further GSEA analyses revealed a significant association of our multiple-dataset dysregulated genes with genes related to key PD clinical symptoms, including bradykinesia, rigidity, akinesia, dyskinesia and postural instability. (Fig. 3F  and Supplementary Table  6 ). Genes associated with resting tremors were not significantly enriched; however, it should be noted that this early and cardinal symptom of PD is absent in ~30% of patients  82.
Importantly, the associations with parkinsonism, dementia and PD-like phenotypes were completely abolished when the genedysregulation score relationship was eliminated by random permutation of the gene labels (Supplementary Fig. 2 ). Taken together, our integrative analysis of multiple datasets of PD patient-reprogrammed neurons reveals a gene expression signature that captures known phenotypic features of the disease. Perturbed PD transcriptomic pathways integrated across datasets A broad range of cellular functions may be impaired in PD. Therapeutically, it remains challenging to predict if one drug targeting a specific pathway in a rare genetic type of PD (e. g. , LRRK2-G2019S) will also stop the pathology in other pathways and across the broad spectrum of PD patients. Pre-clinical transcriptomics analysis of bioengineered neurons from patients' cells is attractive to advance this issue because it can assess the dysregulation of all pathways at once in live human cells with minimal bias.
Therefore, we aimed to identify the biological pathways that are overrepresented among the dysregulated transcripts in PD midbrain cells integrated across datasets. First, we performed a pathway enrichment analysis with GSEA  83. This analysis revealed significant (Benjamini-Hochberg FDR-q < 0. 01) enrichment of 579 pathway gene sets, 408 (70%) of which could be annotated to one of six main biological/functional themes with previously implicated roles in PD pathogenesis (Fig. 4  and Supplementary Table  7 ). "Neurotransmission and synaptic function" was the major enriched biological theme, comprised of 118 pathways with functions in synaptic transmission, ion channel regulation, neuronal excitability, and intracellular signalling. Other enriched pathways were related to cytoskeleton and neuromorphogenesis, energy and metabolism, cellular and oxidative stress responses, inflammation and immunity, and intracellular trafficking (Fig.
Synaptic dysfunction of PD patient reprogrammed neurons Pathways involved in neurotransmission and synaptic function comprised the largest group of pathways transcriptomically dysregulated across all the datasets (Fig. 4 ). We also observed a significant overrepresentation of genes related to ontology linked to "abnormal central nervous system electrophysiology" among the genes with the highest dysregulation in PD (top ranked D overall score) (Fig. 5A ). We confirmed the synaptic dysfunction with patchclamping by comparing the electrophysiological and synaptic properties of 169 "mature" (Type 5 definition based on  40  ) neurons from a subset of patients (n = 2) and controls (n = 2) (Fig. 5B-M ). We selected for patch-clamping the cells with typical neuronal morphology and evident synapsin:GFP expression following lentiviral vector transduction after maturation in BrainPhys neuronal medium (Fig.
Therefore, we do not know if the synaptic dysfunction occurs upstream or downstream of the pathology. Nevertheless, these results demonstrate the value of transcriptomics assays on reprogrammed neurons to assess the health of intrinsically connected cellular pathways and further implicate synaptic dysfunction in PD pathology driven by genetic predispositions. Prediction of disease-modifying therapeutics for PD based on perturbed transcriptomic pathways All current PD treatments pivot on restoring dopamine levels in the brain to reduce the severity of symptoms. PD is a chronic neurodegenerative disorder. However, no available treatments can halt or slow the accelerated cellular loss. To identify potential drug candidates based on the PD transcriptional signature from the reprogrammed neurons, we performed a GSEA of the ranked list of dysregulated genes against DrugBank's database of 5,724 drug-target gene associations  90.
The majority of these trials (n = 88, 61%) focus on symptomatic relief; the remaining (n = 57, 39%) employ a disease-modifying strategy. We performed a literature search for each therapeutic and linked its molecular function(s) to the transcriptomic pathway(s) identified as dysregulated in iPSC-derived PD neurons (Fig. 6B-D ). This analysis revealed that most clinical trials for disease-modifying therapies attempt to combat cellular and oxidative stress by targeting alpha-synuclein and clearing reactive oxygen species, reducing inflammation or increasing mitochondrial function (Fig. 6B, C ). Overall, our data further support the rationale of these current clinical trials that target dysregulated pathways across our PD transcriptomics datasets. In particular, our observation that synaptic transmission is dysregulated at both the transcriptional and functional levels in PD reprogrammed neurons further reinforces the need for clinical trials aiming to improve neuronal synaptic function.
Significant variation between neuronal transcriptomics of genetic and sporadic cases and lack of clustering was also reported previously  39. To the best of our knowledge  109  , this is the most extensive database exploring the transcriptomic profiles of PD using patient-derived reprogrammed neurons to date. As such, this study will serve as a solid starting point, which can be refined in the future with new data. The neural stem cell reprogramming field has rapidly proliferated since the seminal discovery of iPSCs  110, 111. All agree that more rigorous technical harmonization between laboratories would increase reproducibility. However, researchers continue to improve the protocols for neural reprogramming, differentiation and maturation, and a gold-standard consensus has not been reached yet.
For the moment, different methods have advantages and limitations, which may be more or less suited for specific laboratories or projects  112. Therefore, in this study, we compared and combined the transcriptomics signature from PD patients using the two major neural reprogramming strategies from fibroblasts: iPSC reprogramming and direct conversion. Midbrain and dopaminergic neuronal profiles were enriched in all the datasets included in our study. Accordingly, the transcriptomes were remarkably similar to adult brain biopsies for the midbrain substantia nigra and striatum. In our integrative transcriptome analysis, we prioritized genes expressed in adult human substantia nigra to further increase clinical relevance and reduce cellular reprogramming biases. Despite the significant progress in human neuronal reprogramming technologies, it is evident that human brain tissue in vitro cannot fully recapitulate the complexity of the adult brain in vivo.
20-W0500). Human pluripotent stem cell culture and midbrain neuronal differentiation Primary fibroblast cultures were derived from participants' dermal skin punch biopsies and reprogrammed either by retroviral delivery or non-integrating (Sendai virus or episomal vectors) methods in different laboratories. Subject details and source information are provided in Supplementary Table  1. The differential analysis in this paper was performed on six independent datasets from human midbrain neuronal cultures generated either through iPSC reprogramming (sc-iPS-PatchSeq, sc-iPS-10XSeq, bulk-iPS-Dopa, bulk-iPS-Mixed) or direct fibroblast conversion (bulk-iN-Mixed, bulk-iN-Dopa) as follows: Midbrain cultures for sc-iPS-PatchSeq dataset. Human induced pluripotent stem cells and WA09 (H9) embryonic stem cells (WiCell, Wisconsin, U.
S. A. ) were maintained in mTeSR™1 medium (STEMCELL Technologies #85850) on cell culture ware coated with human Embryonic Stem Cell (hESC)-qualified Matrigel (Corning #354277) as per manufacturer's instructions. Differentiation of pluripotent cells into neural progenitor cells (NPCs) was performed using an embryoid body (EB)-based protocol as described previously  113, 114. NPCs were expanded for three to five passages (split ratio of 1:2-1:3 per passage) on Matrigel-coated six-well plates and cryobanked in vapour nitrogen. For experiments, cells were thawed and expanded for at least one extra passage in neural progenitor medium (NPM). NPM was composed of DMEM/ Fig. 6  Prediction of therapeutics for Parkinson's disease based on dysregulated transcriptomic pathways. A GSEA against DrugBank's database of drug-target gene associations  90  (n = 5,724 gene sets) identifies 18 therapeutic drugs, each associated with a minimum of 15 target genes, that are significantly (FDR-q < 0.
Twenty-four hours later, the cells were switched gradually (half medium change) to neuronal maturation medium (NMM): BrainPhys™ basal medium  114  supplemented with 1× N2 (Thermo Fisher Scientific #17502-048), 1× B27 (Thermo Fisher Scientific #17504-044), BDNF (20 ng/mL, Thermo Fisher Scientific #450-02), GDNF (20 ng/mL, PeproTech #450-10), dibutyryl cyclic AMP (1 mM, Sigma-Aldrich #D0627), ascorbic acid (200 nM, Sigma-Aldrich #A0278), and laminin (1 μg/mL, Thermo Fisher Scientific #23017015). Half of the neuronal medium was gently replaced three times a week. Plates were kept in a humidified incubator at 37 °C with 5% CO 2 and 21% O 2. The osmolarity (~300-305 mOsmol/L) and pH (~7. 3-7. 4) of the medium were maintained constant over time. Midbrain cultures for sc-iPS-10XSeq dataset  38. Details regarding the dopaminergic neuronal differentiation of wild-type (WT) and isogenic SNCA-A53T iPSCs are available in the original publication  38.
Midbrain cultures for bulk-iPS-Dopa dataset. The neuronal cultures for the bulk-iPSC-Dopa dataset were generated following a published protocol  115. Briefly, small molecules/factors (SB431542, LDN193-189, purmorphamine, FGF-8, SAG, CHIR99021, BDNF, GDNF, TGFb, ascorbic acid, cAMP) were used to differentiate iPSCs into midbrain DA neurons. These cells were not sorted but instead simply peeled off using a three to five minutes Accutase treatment at room temperature. Neuronal cultures for bulk-iPS-Mixed dataset  31. Details regarding iPSC maintenance and midbrain dopaminergic neuron differentiation are available in the original publication  31. Briefly, iPSCs were maintained in E8 medium on Matrigel and passaged every five days (split ratio of 1:6-1:12 per passage) using Versene. For neuronal differentiation, iPSCs were grown to ~80% confluency, singularized using Accutase (Millipore/Sigma #SCR005) for 5 min at 37 °C, and plated as a fully confluent monolayer (density of 2 × 10 5 cells/cm 2 ) onto Matrigel-coated 6-well plates in E8 medium containing Y-27632 (5 μM, StemGent).
On day 15, cultures were dissociated to single cells using Accutase (20 min at 37 °C), re-suspended in maturation medium (50% DMEM/F12 and 50% Neurobasal supplemented with 1× N2, 1× B27-vitamin A, BDNF, GDNF, dbCAMP, L-ascorbic acid, DAPT and TGF-β3) plus Y-27632 (5 μM), and re-plated onto Matrigel-coated culture ware (density of 2 × 10 5 cells per cm 2 ) or coverslips (density of 2 × 10 5 cells per 50-μl drop). Cells were allowed to attach for 45 min at 37 °C, and maturation medium was then added to a final volume of 3 or 1. 5 mL/well for, respectively, six-well plates and 24-well plates with coverslips. The medium was changed 48 hours after seeding and gently replaced every three days until day 30. Neuronal cultures for bulk-iN-Mixed dataset  39. Primary human dermal fibroblasts were directly reprogrammed into induced neurons (iNs) as previously described  39, 116.
In particular, after three weeks of iN conversion, cells were detached using TrypLE and stained for PSA-NCAM (1:100, Milteny) for 45 min at 4 °C in sorting buffer (PBS containing 1% KOSR, 250 mM myo-inositol and 5 μg/ml polyvinyl alcohol [PVA]). Cells were washed, stained with Alexa-647conjugated anti-mouse IgM secondary Ab for 30 min at 4°, resuspended in sorting buffer supplemented with EDTA and DNase, and filtered through a 40-μm cell strainer. Alexa-647positive cells were sorted directly into TRIzol LS Reagent, and RNA was isolated and digested using TURBO DNase (Thermo Fisher Scientific) following manufacturer's instructions. Neuronal cultures for bulk-iN-Dopa dataset. Method for iN-Dopa conversion is based on the same as iN-mixed protocol, adding an extra lentiviral vector XTP-Lmx1a to the transcriptional factor cocktail and purmorphomine (2 μM; Adooq Bioscience), SAG(0.
Sample preparation for RNA-seq Three sample preparation methods (bulk RNAseq, single-cell RNAseq and Patch-seq) were used as follows: RNAseq sample preparation for sc-iPS-PatchSeq dataset. Individual coverslips containing neurons were transferred into a heated (25 °C) recording chamber and continuously perfused (1 ml•min -1 ) with either BrainPhys™ basal medium  114  or artificial cerebrospinal fluid (ACSF) bubbled with a mixture of CO 2 (5%) and O 2 (95%). ACSF composition was adjusted to match the inorganic salt concentration and osmolarity of BrainPhys™ basal and contained: 121 mM NaCl, 4. 2 mM KCl, 1. 1 mM CaCl 2 , 1 mM MgSO 4 (or 0. 4 mM MgSO 4 and 0. 3 mM MgCl), 29 mM NaHCO 3 , 0. 45 mM NaH 2 PO 4 -H 2 O, 0. 5 mM Na 2 HPO 4 and 20 mM D-glucose (all chemicals from Sigma-Aldrich). Whole patch-clamp electrophysiological recordings were performed on single neurons infected with and expressing Synapsin:GFP lentiviral vector according to the protocol detailed below under "Patch-clamp recordings".
cDNA samples were assessed for quality (2100 Bioanalyzer, Agilent) and quantity (Qubit fluorometer, Thermo Fisher Scientific), and construction of mRNA-seq libraries was performed with 0. 25 ng of input cDNA using Nextera XT DNA sample prep reagents (Illumina #FC-131-1096). Library fragments were size-selected, purified, quantified and examined for the correct size (2200 TapeStation High Sensitivity D1K ScreenTape Assay, Agilent #5067-5363), and equimolar amounts of uniquely barcoded libraries were multiplexed and sequenced on a HiSeq 2000 or 2500 platform (Illumina) using 100-bp paired-end sequencing. Only functionally mature neurons were included in the analysis (AP types 4 and 5 based on  40  ). RNAseq sample preparation for sc-iPS-10XSeq dataset  38. Details regarding the neuronal sample preparation for single-cell transcriptomics with the 10x Genomics Chromium™ system are available in the original publication  38.
Briefly, after three weeks of differentiation into dopaminergic neurons, cells were dissociated using Accutase (Thermo Fisher Scientific), counted, and assessed for viability. Cell suspensions (8000 viable single cells) were loaded onto a 10x Chromium instrument (10x Genomics) according to the manufacturer's protocol. RNAseq sample preparation for bulk-iPS-Mixed dataset  31. mRNA was isolated in triplicate for each differentiated cell line and prepared for sequencing as per previously described methods in the original publication  31, 119. RNA samples were assessed for quality (2100 Bioanalyzer, Agilent) and quantity (Qubit fluorometer, Thermo Fisher Scientific), and up to 1 μg of total RNA per sample was processed for cDNA synthesis, amplification and library construction using the TruSeq Stranded mRNA library preparation kit (Illumina).
Constructed libraries were purified using Agencourt AMPure XP beads (Beckman Coulter #A63881), quantified for yield (Qubit fluorometer), and quality-assessed by fragment analysis (2100 Bioanalyzer). Sample libraries were then multiplexed and sequenced on a NextSeq 500 sequencer (Illumina) using 75-bp single-end SBS chemistry. RNAseq sample preparation for bulk-iPS-Dopa, bulk-iN-Mixed and bulk-iN-Dopa datasets. mRNA-seq libraries were constructed using the TruSeq Stranded mRNA Sample Prep kit following the manufacturer's protocol (Illumina) and sequenced on a HiSeq 2500 platform (Illumina) using 50-bp single-end SBS chemistry. Bioinformatics RNA-seq data processing, expression quantification, and quality control All transcriptomics data was processed from raw FASTQ files. Bulk RNA-seq data.