We propose to use cRFP (common Repository of FBS Proteins) in the MS (mass spectrometry) raw data search of cell secretomes. cRFP is a small supplementary sequence list of highly abundant fetal bovine serum proteins added to the reference database in use. The aim behind using cRFP is to prevent the contaminant FBS proteins from being misidentified as other proteins in the reference database, just as we would use cRAP (common Repository of Adventitious Proteins) to prevent contaminant proteins present either by accident or through unavoidable contacts from being misidentified as other proteins. We expect it to be widely used in experiments where the proteins are obtained from serum-free media after thorough washing of the cells, or from a complex media such as SILAC, or from extracellular vesicles directly.

Autism spectrum disorder (ASD) is defined by common behavioral characteristics, raising the possibility of shared pathogenic mechanisms. Yet, vast clinical and etiological heterogeneity suggests personalized phenotypes. Surprisingly, our iPSC studies find that six individuals from two distinct ASD subtypes, idiopathic and 16p11.2 deletion, have common reductions in neural precursor cell (NPC) neurite outgrowth and migration even though whole genome sequencing demonstrates no genetic overlap between the datasets. To identify signaling differences that may contribute to these developmental defects, an unbiased phospho-(p)-proteome screen was performed. Surprisingly, despite the genetic heterogeneity, hundreds of shared p-peptides were identified between autism subtypes including the mTOR pathway. mTOR signaling alterations were confirmed in all NPCs across both ASD subtypes and mTOR modulation rescued ASD phenotypes and reproduced autism NPC-associated phenotypes in control NPCs. Thus, our studies demonstrate that genetically distinct ASD subtypes have common defects in neurite outgrowth and migration which are driven by the shared pathogenic mechanism of mTOR signaling dysregulation. Methods The entire dataset represents source data for the Figures presented in our manuscript published in the journal Elife " Dysregulation of mTOR signaling mediates common neurite and migration defects in both idiopathic and 16p11.2 deletion autism neural precursor cells”. The source data is a requirement for publication in Elife and will aid others who would like to replicate the Figures and potentially utilize the data in their future assessments. With this in mind, much of the methods utilized in collecting our data and the nomenclature of the files in this data bank can largely be contextualized by referencing our manuscript. For example, Figure1B_SourceData is the data utilized to create Figure 1B in the above manuscript. The manuscript focused on cell culture data from a bench lab setting. Specifically, this work focused on cells known as neural precursor cells (NPCs), which are early brain cells that ultimately go on to develop into neurons, astrocytes, and oligodendrocytes (the mature cells of the brain). Specifically, these neural precursor cells were derived from individuals with autism and either sibling or unrelated controls (from the NIH) utilizing a technique known as induced pluripotent stem cells (iPSCs). Briefly, white blood cells or fibroblast cells from individuals with autism were reprogrammed into induced pluripotent stem cells using a viral technique and then these induced pluripotent stem cells were grown in a special medium that allows them to form into neural precursor cells. These neural precursor cells were cultured in dishes and then utilized in various ways for our experiments. The goal was to study whether early developmental processes were disrupted in NPCs derived from individuals with Autism Our source data includes data from NPCs derived from 12 different individuals. 6 of these individuals have autism- 3 with idiopathic autism (underlying genetic cause is unknown) who have been labeled as I-ASD in this data set, and 3 with a genetic form of autism caused by the 16p11.2 deletion notated as 16pDel in this data set. The comparison groups include a sex-matched sibling to the I-ASD cohort and then 3 unrelated sex-matched individuals from the NIH who have no known neuropsychiatric disorders. Our data is presented either as by individual or by group, depending on how the data is displayed in our manuscript figure. The overall groups are then: I-ASD (idiopathic autism), Sib (Sibling to the I-ASD individual who does not have autism or other developmental disorders), 16pDel (Individuals with autism and the 16p11.2 deletion), and then NIH (for the 3 individuals whose stem cells were taken from an NIH repository). Individuals were notated: I-ASD-1, I-ASD-2, I-ASD-3, Sib-1, Sib-2, Sib-3, NIH-1, NIH-2, NIH-3, 16pDelM-1, 16pDelM-2, and 16pDelF, or as “individual 1, individual 2, and individual 3” across the datasets. In addition, other notations seen include “Clone”. When induced pluripotent stem cells are made from the initial cell derived from an individual, they form clusters of cells known as clones. Due to a variety of different reasons, clones can have different properties from one another, so to increase rigor, often used 2-5 different clones from each of the above individuals were made into NPCs to conduct our experiments. Clones can be considered to be distinct biological replicates and their notation is important for the case of some experiments. We did not do the same number of clones or experiments for every individual. This is because we established this technique on the I-ASD cohort first, specifically in Sib-1 and I-ASD-1 and thus there are more replicates in this group than in any other. There are several types of NPC-related data and the methods for these are described below with links for further information. Neurite outgrowth data: As a neural precursor cell grows and develops, one of the important processes it needs to undergo is differentiation so it can form into a cell such as a neuron. Neurons are characterized by the presence of axons and dendrites which are outgrowths from neuronal cells that allow them to communicate with each other and other cells. NPCs are not quiet neurons yet, but as they develop, they begin to grow a neurite which is an early version of an axon or a dendrite. One set of our data includes the study of these neurites, and the methods were as follows: NPCs growing in a dish over time will cover the entire surface area of this dish (this is called confluency). When the NPCs are confluent, they are ready for experimentation. Confluent NPCs were taken out of their growth dish and then replated at the low density of 50,000 cells into alternative dishes. These replated NPCs were allowed to sit in these new dishes for 48 hours. At 48 hours, dishes were imaged on a confocal microscope which allows us to see each NPC. 3 sections of each dish (about 1 cm of the dish) were counted (visually by eye) for the total number of NPCs in that area and then we also kept count of the number of NPCs that had neurites (which were described as extension coming from the cell body that was twice the length of the cell body of the NPC). The percentage of neurites in each area was averaged together to get an estimate of the total neurites (%) in a dish. This is the data found in all the neurite Excel sheets. For more step-by-step information on the neurite assay, please see our methods paper in JOVE. In some cases, when neurites were replated at low density for the experiment, we placed them under different media “conditions” (notated as “conditions”) in the Excel sheets. The altered media conditions included the addition of either a growth factor, neurotransmitter, or signaling molecule (extracellular factor: ex: PACAP, NGF, 5-HT) or a small molecule drug (SC-79, MK-2206) to the media. Neurosphere migration data: Another important developmental process that NPCs undergo is migration. To assess NPC migration, we first created structures known as neurospheres which are essentially spherical aggregates of NPCs. When we take these spheres and plate them onto a gel coating, over time, some cells will migrate away from the original sphere structure and the ultimate result will look like a carpet of migratory cells spreading out from a dense inner cell mass. To measure migration, we measure the total area of all the cells and subtract from it the area of the inner cell mass size. Thus, migration is represented in um2 as indicated in many of our Excel sheets. Like with the neurite experiments, during the migration phase, different media conditions were used. A detailed step-by-step guide on this assay is also seen in our JOVE visual methods manuscript. Western Blot data: Western blotting is a semi-quantitative method to assess the presence and amount of a protein. Our proteins of interest are part of a signaling pathway known as the "mTOR pathway" which is an important pathway for regulating development. For our data, the protein was extracted from our NPCs and then this protein was ultimately put through the western blotting technique (which is a quite standard method, and our exact methods are detailed in the manuscript). Once the blot is acquired, immunochemistry and chemiluminescence techniques were utilized to develop an X-ray film that shows an imprint of the protein of interest. We have provided scanned images of these X-ray films as one part of our Western Blot Source data. The lanes (black bars) from these films can be measured as a proxy for protein amount. The size and intensity of the lanes roughly translate to a semi-quantitative measure known as densitometry and give an estimate of how much of the protein of interest is present in our NPCs. ImageJ software was used to measure these bands and obtain a value. The proteins of interest were P-S6, S6, P-AKT, and AKT (all mTOR pathway members). A ratio between the phosphorylated version (P-S6) and the unphosphorylated version (S6) was provided as the data in our Excel sheets. Much like the experiments above, NPCs could be cultured in a base culture medium or culture medium with the addition of a drug or an extracellular factor Omics Data: Several types of omic data were also collected including genomic, phosphoproteomic, and proteomic data. These data were acquired from other collaborating labs or clusters at Rutgers that have standardized protocols and methods for obtaining these large datasets. Brief non-proprietary methods are included in the original manuscript. Interpretation of this data does not require an understanding of these methods. The protein or gene names and either the gnomad frequency or the logPvalues were fed into software from Qiagen known as Ingenuity to allow for the pathway analysis and network

Mitochondria are undeniably the cell powerhouse, directly affecting cell survival and fate. Growing evidence suggest that mitochondrial protein repertoire affects metabolic activity and plays an important role in determining cell proliferation/differentiation or quiescence shift. Consequently, the bioenergetic status of a cell is associated with the quality and abundance of the mitochondrial populations and proteomes. Mitochondrial morphology changes in the development of different cellular functions associated with metabolic switches. It is therefore reasonable to speculate that different cell lines do contain different mitochondrial-associated proteins, and the investigation of these pools may well represent a source for mining missing proteins (MPs). A very effective approach to increase the number of IDs through mass spectrometry consists of reducing the complexity of the biological samples by fractionation. The present study aims at investigating the mitochondrial proteome of five phenotypically different cell lines, possibly expressing some of the MPs, through an enrichment–fractionation approach at the organelle and protein level. We demonstrate a substantial increase in the proteome coverage, which, in turn, increases the likelihood of detecting low abundant proteins, often falling in the category of MPs, and resulting, for the present study, in the identification of METTL12, FAM163A, and RGS13. All MS data have been deposited to the MassIVE data repository (https://massive.ucsd.edu) with the data set identifier MSV000082409 and PXD010446.

BackgroundStudies regarding differentially expressed genes (DEGs) in Parkinson’s disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies.PurposeMeta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner.MethodsSix microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer’s disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures.ResultsOur meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes.ConclusionDownregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.