dataesr/scientific-paragraphs-categorization huggingface dataset:
Hypermetabolism is a global state of the cell, and no currently available approach can selectively manipulate or correct hypermetabolism without introducing unresolvable confounds. For this reason, it is currently not possible to mechanistically test this assertion. We speculate that the diversion of energetic resources, as well as substrates including nucleotides  76  , may contribute to reduced DNA replication fidelity, which in turn could contribute to both mtDNA instability and telomere attrition, independent of cell division. Fourth, our longitudinal RNASeq and DNAm datasets reveal conserved recalibrations implicating developmental and translation-related pathways, as well as cell-cell communication, with OxPhos dysfunction and hypermetabolism. These identified pathways overlap with previously identified multi-omic overrepresentation analysis performed on iPSC-derived neurons from SURF1 patients  90.
Limitations. A major open question relates to the origin and modifiability of signaling pathway(s) and cellular process(es) that underlie hypermetabolism in OxPhos deficient cells and humans. Rather than pursuing a single potential explanation, here we attempted to deeply phenotype both cellular models of hypermetabolism and to produce a foundational dataset covering several key processes and pathways previously implicated in the pathogenesis of OxPhos defects. Our dataset therefore provides a foundation that can be used as a resource to develop targeted, mechanistic experiments to i) determine the origin and modifiability of hypermetabolism in the context of OxPhos defects in vitro and in vivo, and ii) resolve the mechanism(s) linking hypermetabolism to human aging biology. The small sample size and the in vitro nature of the cellular data could be regarded as limitations of this work, potentially limiting generalizability.
Mycoplasma testing Mycoplasma testing was performed according to the manufacturer's instructions (R&D Systems #CUL001B) at the end of lifespan for each treatment and cell line used. All tests were negative. The final Seahorse injection included Hoechst nuclear fluorescent stain (ThermoFisher Scientific #62249) to allow for automatic cell counting. After each run, cell nuclei were counted automatically using the Cytation1 Cell Imager (BioTek) and raw bioenergetic measurements were normalized to relative cell counts on a per-well basis. ATP metrics were determined using the P/O ratios of OxPhos and glycolysis as previously described by Mookerjee et al. 57. These conversions assumed energy sourced was derived entirely by glucose. All JATP measurements take into account non-mitochondrial and proton leak derived oxygen consumption thereby reflecting the mitochondrial ATP-synthesis related flux (Extended Data Figure  2A ).
Calculations of energy expenditure and normalization to division rate and cell size To assess if increased ECAR in experimental conditions were due to non-glycolytic activity, a glucose-dependency test was performed using the Seahorse XF Glycolysis Stress Test Kit (Agilent, 103020-100). Prior to extracellular flux measurements, young healthy control (Donor2) and young SURF1 (Patient3) cells were grown overnight in differing nutrient conditions: physiological 5. 5mM glucose, 0mM glucose, 25mM glucose. The glycolysis stress test kit was performed according to the manufacturer's protocol. To monitor growth and cell death, cells were cultured for 7 days in each glucose condition and monitored daily (see Extended Data Figure  3 ). NGS Libraries were generated using an enzymatic DNA fragmentation approach using Ion Xpress Plus Fragment Library Kit.
Libraries were diluted at 100 pM before sequencing and pooled by a maximum of 25 samples. Sequencing was performed using an Ion Torrent S5XL platform using Ion 540 chipTM. Signal processing and base calling were done by the pre-processing embedded pipeline. Demultiplexed reads were mapped according to the mtDNA reference sequence (NC_012920. 1) before being analysed with a dedicated homemade pipeline including eKLIPse (Goudenège et al, GIM, 2019; https://github. com/dooguypapua/eKLIPse) using the following settings. Deletion counts were estimated with a variant call cutoff of >5% heteroplasmy, and separately with cutoffs of 1% and 5% heteroplasmy (see Extended Data Figure  8D ). Triplicate values for each sample were averaged for mtDNA and nDNA. Ct values >33 were discarded. For triplicates with a C.
44. 0)  118. These data were imported using txi import ('tximport', v1. 18. 0, length-scaled TPM), and vst normalized ('DEseq2', v1. 30. 1). Dimensionality reduction was performed using 'Rtsne' (v0. 15) with perplexity value of 10 and initial dimensions of 30 on the log2 transformed normalized expression values after removing genes without any variation in expression across all samples. Linear mixed modeling was performed using the 'lme4' (v1. 1) R package with the fixed effects of time grown and clinical group for SURF1-differential expression and fixed effects of time grown and treatment with a mixed effects of the cell line for Oligodifferential expression. P values were obtained by running an Anova comparing the model for each gene to a null model that had a fixed effect of days grown (mixed effects of cell line for Oligo models) and then the value was corrected for multiple comparisons using FDR-adjustment (p<0.
DNA methylation and methylome analysis Global DNA methylation was measured using the Illumina EPIC microarray ran at the UCLA Neuroscience Genomic Core (UNGC). DNA was extracted using the DNeasy kit (Qiagen cat#69506) according to the manufacturer's protocol and quantified using QUBIT broad range kit (Thermo Fisher Scientific cat#Q32852). At least 375 ng of DNA was submitted in 30 µl of ddH2O to UNGC for bisulfite conversion and hybridization using the Infinium Methylation EPIC BeadChip kit. Sample positions across plates were randomized to avoid batch variation effects on group or time-based comparisons. All DNA methylation data were processed in R (v4. 0. 2), using the 'minfi' package (v1. 36. 0). Quality control preprocessing was applied by checking for correct sex prediction, probe quality, sample intensities, and excluding SNPs and non-CpG probes.
Data were then normalized using Functional Normalization. Using the R package 'sva' (v3. 12. 0), both RCP and ComBat adjustments were applied to correct for probe-type and plate bias, respectively. After quality control, DNAm levels were quantified for 865,817 CpG Sites. Dimensionality reduction was performed using the 'Rtsne' package (v0. 15) with a perplexity value of 10 and initial dimensions of 30 on the normalized beta values. We ran linear mixed effects models (LMER) using 'lme4' (v1. 1). For our differential methylation analysis of SURF1, the fixed effects were assigned to 'days_grown' and 'clinical_group'. For the Oligomycin treatment, the fixed effects 'time_grown' and 'treatment' and the mixed effect was assigned of the 'cell_lines' (i. e. , donors). P values were obtained from an analysis of variance (ANOVA) comparing the model for each CpG to a null model with a fixed effect of days grown (mixed effects of cell line for Oligomycin models) and then corrected for multiple comparisons using FDR-adjustment (p < 0.
05) to identify differentially methylated CpGs (DMPs). Differentially methylated regions (DMRs) were derived using the modified comb-p method in the 'Enmix' package (v1. 26. 8), with a maximum distance for DMR combination of 1000bp, a bin size for autocorrelation of 310, and FDR-adjustment cutoff of 0. 01, and minimum of 3 CpGs per a DMR. Each DMP and DMR were assigned to the nearest annotated gene (IlluminaHumanMethylationEPICanno. ilm10b4. hg19 package, v0. 6. 0). Gene set enrichment analysis was then performed using ShinyGO 119 (v0. 66, http://bioinformatics. sdstate. edu/go/) on the top 1,000 DMPs-or DMRs-associated genes based on the combined negative log p value across hyper-and hypo-methylated DMPs and DMRs.
DNAmAge DNAmAge was calculated using the online calculator (https://dnamage. genetics. ucla. edu/new) with normalization using the age of cell line donor as the input age. This outputted the Horvath1 (i. e. PanTissue clock), Horvath2 (Skin&Blood clock), PhenoAge, Hannum, and GrimAge estimated DNAmAges. PC-based DNAmAges were then obtained using the principal component method (https://github. com/MorganLevineLab/PC-Clocks)  84. The rates of epigenetic aging for each cell line were determined from the linear slope of timepoints between 25 to 75 days. This period ensures that Oligo treatment has taken effect, and avoids late-life changes in the behavior of DNAm clocks, providing the time window where the signal is most stable.
(C-D) Cell-free mitochondrial DNA per nuclear DNA across the cellular lifespan trajectories (C) and averages (D). Data are mean ± SEM. * P < 0. 05, ** P < 0. 01, *** P < 0. 001, **** P < 0. 0001, mixed effects model (fixed effect of clinical condition and days grown, random effects of cell line). Ribosomal gene expression. (A) Heatmap of ribosomal gene expression. Ribosomal genes were selected from the KEGG database (https://www. genome. jp/kegg/pathway/hsa/hsa03010. html). Values are derived from normalized expression centered to the median of the youngest control timepoints. (B) Barplot of ribosomal gene expression between control, SURF1-mutant, and oligo-treated timepoints. Each datapoint is the median normalized expression across all ribosomal subunit genes. Data are mean ± SEM. * P < 0.
Acknowledgements We are grateful to  Jane Newman ,  Renae Stefanetti ,  Robert W Taylor , and  Gráinne S Gorman  ( Wellcome Center for Mitochondrial Research ) for contributing data for Cohort 2,  Rohit Sharma  and  Vamsi Mootha  ( Massachusetts General Hospital ) for contributing data for Cohort 4,  Robert McFarland  ( Wellcome Center for Mitochondrial Research ) for contributing data for Cohort 17, and other investigators whose work contributed to the meta-analysis in Figure 1. We thank  Marlon McGill  for technical assistance with parts of this project. The cellular studies and analyses were supported by  NIH  grant  AG066828  and the  Baszucki Brain Research Fund  to M. P. , the  J. Willard and Alice S. Marriott Foundation ,  Muscular Dystrophy Association, Nicholas Nunno Foundation, JDF Fund for Mitochondrial Research, and Shuman Mitochondrial Disease Fund  to  M.
Data availability statement The RNAseq and DNA methylation datasets for this project are available under the GEO SuperSeries GSE179849. All data preprocessing and analysis code is available on GitHub (https://github. com/gav-sturm/Cellular_Lifespan_Study). Data presented in this manuscript was generated as part of the Cellular Lifespan Study, which includes metabolic and endocrine experimental treatments across multiple donors described in detail in  115. The complete fibroblast dataset for the present study is available without restriction and can be accessed, visualized, and downloaded using our webtool: https://columbia-picard. shinyapps. io/shinyapp-Lifespan_Study/. The meta-analyzed clinical data of mitochondrial disease cohorts (Figure  1 ) can be obtained from the original publications listed in Table  1.
Extended Data Table 2. Genotyping results of SURF1 patient-derived fibroblasts Cell line Surf1 mutation Exonic function dbSNP id Clinical significance* Results from whole genome sequencing (WGS). * Clinical interpretation of genetic variants is based on ANNOVAR geneannotation pipeline that uses ClinVar database as a primary reference. SNV, single nucleotide variant. c. T280C (p. L94L) synonymous SNV rs28615629 Benign | Likely Benign Cell Line Tissue Genotype Sex Age Passage* Source Cat # Donor 1 Dermal breast Normal male 18 1 Lifeline Cell Technology FC-0024 Lot # 03099 Donor 2 Dermal breast Normal female 18 1 Lifeline Cell Technology FC-0024 Lot # 00967 Donor 3 Foreskin Normal male 0 4 Coriell Institute AG01439 Patient 1 Dermal upper-arm skin SURF1 mutation male 0. 25 7 Hirano lab NA Patient 2 Dermal upper-arm skin SURF1 mutation male 11 5 Hirano lab NA Patient 3 Dermal upper-arm skin SURF1 mutation female 9 9 Hirano lab NA Patient 1 c.
Results To illustrate the problem and to demonstrate the software, whole mouse brain images were acquired following retrograde rabies virus labelling. Viral injections were performed into visual or retrosplenial cortex, causing thousands of cells to be cytoplasmically labelled throughout the brain. Data was acquired using serial two-photon microscopy as previously described  [19]  (Fig  1 ). Briefly, coronal sections are imaged at high-resolution (2 μm x 2 μm x 5 μm voxel size) and stitched to provide a complete coronal section. This is carried out for ten imaging planes after which a microtome removes the most superficial 50 μm of tissue and the process is repeated until the entire brain data set is collected. Light emitted from the specimen is filtered The tissue is excited using a femtosecond Ti-sapphire laser (emission wavelength = 800 nm).
For data collection, 50 μm of tissue (at approximately 40 μm to 90 μm below the tissue surface) is imaged in ten, 5 μm thick planes. An in-built microtome then physically removes a 50 μm thick section from the optical face. This process is repeated to generate a complete 3D dataset of the specimen. B: For signal collection, the emitted lightpath is split into two channels whereby the primary channel detects the fluorescence signal of interest from labelled cells (e. g. mCherry at 610 nm) and the second channel (e. g. at 450 nm) detects the tissue autofluorescence signal that reveals gross anatomical structure. https://doi. org/10. 1371/journal. pcbi. 1009074. g001 and detected via at least two channels, a primary signal channel containing the fluorescence signal from labelled target neurons and a secondary 'autofluorescence' channel that does not contain target signals but provides anatomical outlines.
The trained classification network is then applied to classify the cell candidates from the initial detection step (Fig  2F ). The artefacts (such as those at the surface of the brain and in vessels) have been correctly rejected, while correctly classifying the labelled cells. To quantify the performance of the classification network, and to assess how much training data is required for good performance, the manually annotated training data was split up into a new training dataset from four brains, and a test dataset from the fifth brain. A new network was trained on subsets of the training data, and performance tested on the fifth brain (15,872 cells and 18,168 non-cells). Fig  3B  shows that relatively little training data was required for good performance on unseen test data, with 95% of cell candidates classified correctly with *7,000 annotated cell candidates.
The pre-trained network was then retrained using these data points and 10% of the data was held back for validation during training. The network was trained until the validation loss function began to plateau, taking 73 minutes. The full cell detection algorithm was then repeated for both brains using the re-trained network on a laptop computer. Total time for cell detection was 83 minutes for brain 1 and 91 minutes for brain 2 (for full timings see Table  2 ). To assign detected cells to a brain region, the Allen Mouse Brain Reference Atlas (ARA  [38] ) annotations were registered to the secondary autofluorescence channel using brainreg  [39], a Python port of the validated aMAP pipeline  [40]. These annotations were overlaid on the raw data (Fig 4A ), and the number of cells in each brain region were reported, allowing for quantitative analysis (Table  3 ).
Effect of varying axial sampling All the data presented was acquired with high axial sampling (5 μm), but this is not always possible or desirable due to imaging time, data storage requirements, or biological considerations such as photobleaching. To assess the performance of the algorithm with varying optical slice thicknesses, we downsampled the data from brain 2, to generate synthetic datasets with axial sampling of 5, 10, 20 and 40 μm, and the algorithm was applied as before. The cell counts were compared to the mean of expert counts (Table  5 ). Performance in terms of absolute cell numbers (best-fit line slope, and Pearson correlation coefficient) was comparable for 5, 10 and 20 μm, although the 5 μm dataset was most highly correlated to the ground truth. The performance on the 40 μm dataset was much worse.
Discussion Mapping the distribution of labelled neurons across the brain is critical for a complete understanding of the information pathways that underlie brain function. Many existing methods for cell detection in whole-brain images rely on classical image processing, which can be affected by noise, and may not detect complex cell morphologies. DNNs can be used for highly sensitive image processing, but often require laborious generation of training data and are prohibitively slow for the analysis of large, 3D images. The presented method here overcomes these limitations by combining traditional image processing methods for speed, with a DNN to improve accuracy. Recent developments in microscopy technology (e. g. [12] ) now allow for quicker, more routine acquisition of whole-brain datasets.
It is important that the image analysis can be carried out in a timely fashion, and without relying on large-scale computing infrastructure. Processing time for the *180GB images in Fig  4  on a laptop was around 90 minutes, so sixteen datasets could be analysed in a single day, much quicker than the sample preparation and imaging steps. Once parameters are optimised, and the classification network is trained, the software can run entirely without user intervention. In traditional DNN approaches for image analysis, generation of training data is often a major bottleneck. While large-scale "citizen science" approaches can be used to generate large amounts of training data  [42] , this is not practical for the majority of applications, e. g. when anatomical expertise is required. Our method overcomes this by requiring only a binary label (cell or non-cell) for each cell candidate in the training dataset, rather than a painstaking 3D outline of each cell.
The software is released with a pre-trained network, and so the network can be re-trained for specific datasets very quickly. The total time to generate the new network used to classify images in Fig  4  was less than two hours, including generating training data and retraining the network. We show that the results of the proposed method compare well to expert manual counts, particularly the correlation between counts in different brain areas. However our results also show the importance of re-training the pre-trained network for new datasets (even if they are superficially similar). It is important to also bear in mind that although we quantified performance of the algorithm across the brain, we did not label every type of cell, and some areas of the brain with densely packed neurons (e. g.
Although the ResNet architecture was chosen based on performance  [36]  and flexibility in new contexts (e. g. [43] ), there are many newer network architectures that could be implemented to improve performance (e. g. [44, 45] ). Different types of network (such as a 3D U-Net  [46] ) could also be used to segment the cell boundaries for additional analyses such as measuring cell size and shape, or quantifying fluorescence expression. Lastly, although this approach was designed for fast analysis of large whole-brain datasets, the proposed two-step approach could be used for any kind of large-scale 3D object detection. This software is fully open-source, and has been written with further development and collaboration in mind. Although the algorithm was designed for neuron detection, the software could be applied to any 3D image data, and we provide a graphical user interface (as a napari  [47]  plugin) to allow easy adoption by as many users as possible.
To generate the data to test the algorithm, data was acquired using a different, custom-built resonant-scanning system controlled by ScanImage (v5. 6, Vidrio Technologies, USA) using BakingTray  [49] , a custom software wrapper for setting up the imaging parameters. Images were assembled using StitchIt  [50]. Both brains were imaged in a single acquisition using a Nikon 16x objective (NA = 0. 8), with a voxel size of 2. 31 μm x 2. 31 μm x 5 μm.
Cell candidate classification using deep learning Cell candidates were classified using a ResNet  [36] , implemented in Keras  [51]  for TensorFlow  [52]. 3D adaptations of all networks from the original paper are implemented in the software (i. e. 18, 34, 50, 101 and 152-layer) and can be chosen by the user, but the 50-layer network was used throughout this study. The general architecture of these networks is shown in S3 and S4 Figs. To generate data to train the classification network, output from the candidate detection step (cell candidate coordinates) were manually classified using a custom FIJI  [14]  plugin, or a napari  [47]  plugin that is supplied with the software. Expert annotators were presented with the raw data, with cell candidates represented by hollow spheres, centered on the cell candidate.
By scrolling through the 2D planes of the 3D dataset, the experts could view the cell candidate in 3D before marking it as a cell or artefact. Candidates were determined to be cells or artefacts based on size, shape (including the presence of neurites) and the fluorescence level compared to the secondary autofluorescence channel (which was visualised at the same time). Candidates were labelled by three experts, labelling different brains, and a subset of labelled candidates were cross-checked between experts. Image cuboids of 50 μm x 50 μm x 100 μm (resampled to 50 x 50 x 20 voxels) were extracted from both the primary signal, and secondary autofluorescence channels, centered on the coordinates of the manually classified cell candidate positions. To increase the size of the training set, data were randomly augmented.
Image registration and segmentation To allow detected cells to be assigned an anatomical label, and for them to be analysed in a common coordinate framework, a reference atlas (Allen Mouse Brain Atlas, ARA,  [38] , provided by the BrainGlobe Atlas API  [54] ) was registered to the autofluorescence channel. This was carried out using brainreg  [39] , a Python port of the automatic mouse atlas propagation (aMAP) software  [40] , which itself relies on the medical image registration library, NiftyReg  [55]. Firstly the sample brain was downsampled to the same voxel spacing as the atlas (10 μm isotropic) and was reoriented to match the atlas template brain. These two images were then filtered to remove-high frequency noise (greyscale opening and flat-field correction). The images were firstly registered using an affine transform (reg_aladin  [56] ), followed by freeform non-linear registration (reg_f3d  [55] ).
Validation To compare results of the algorithm to ground truth, two experts manually annotated each cell in two whole-brain images using the same napari plugin used to generate the training data. The experts were shown the full-resolution images, with both channels displayed in different colors. The experts could scroll through the 3D images plane by plane, zoom in and out, and adjust contrast settings to best visualise cells in different brain areas. Experts annotated cells based on the same criteria as for generating the training data (shape, size and fluorescence intensity). Annotating a cell position displayed a hollow sphere on top of the image, around the cell coordinate. This was visible in multiple image planes to ensure that individual cells were not labelled more than once. The cell classification network was retrained for the new datasets, and the algorithm was run with the default cell candidate detection parameters.