OSCR

Reduced melanocortin tone modulates feeding during pregnancy in mice.

Code ↔ Paper

12 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 12 matches
  1. [1] § Methods › Xenium spatial transcriptomics analysis ↔ R/config.R, lines 95–153 · score 0.87 · histological damage, SCTransform, DESeq2, arcuate nucleus, glial, shrink
  2. [2] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/03_cell_type_assignment.R, lines 217–273 · score 0.83 · Allen Brain, Cell Atlas, Unsupervised clustering, refinement, endothelial, agreement
  3. [3] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/04_neuron_subclustering.R, lines 58–120 · score 0.82 · Silhouette scores, murine arcuate, clustering resolutions, embeddings, canonical, neuroendocrine
  4. [4] § Methods › Xenium spatial transcriptomics analysis ↔ R/singlecell.R, lines 44–58 · score 0.82 · FindClusters, FindNeighbors, RunPCA, standard Seurat, graph, pipeline
  5. [5] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/05_pseudobulk_deseq2.R, lines 1–75 · score 0.79 · dispersion trend, DESeq2 Modeling, ad libitum, moderate, glial, shrink
  6. [6] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/05_pseudobulk_deseq2.R, lines 1–75 · score 0.78 · ComplexHeatmap, split models, DESeq2 model, ad libitum, apeglm, heatmaps
  7. [7] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/03_cell_type_assignment.R, lines 27–62 · score 0.73 · log normalized, singleR, single cell, class, assignment, Hypomap
  8. [8] § Results › Pregnancy alters the transcriptome of AgRP and POMC neurons towards the promotion of positive energy balance ↔ pipeline/04_neuron_subclustering.R, lines 58–120 · score 0.72 · tuberoinfundibular dopamine, dynorphic, kisspeptin, neurokinin, somatostatin, GHRH
  9. [9] § Methods › Xenium spatial transcriptomics analysis ↔ R/config.R, lines 155–200 · score 0.70 · correlation heatmaps, DESeq2 model, max, global, rlog, transformed
  10. [10] § Methods › Xenium spatial transcriptomics analysis ↔ pipeline/01_build_seurat_object.R, lines 1–16 · score 0.65 · median eminence, expert annotation, segmentation, Seurat, pipeline, Xenium
  11. [11] § Results › Pregnancy alters the transcriptome of AgRP and POMC neurons towards the promotion of positive energy balance ↔ R/pseudobulk.R, lines 121–143 · score 0.54 · apeglm shrinkage, fold changes, DESEQ2, transcripts, Gene
  12. [12] § Results › Pregnancy alters the transcriptome of AgRP and POMC neurons towards the promotion of positive energy balance ↔ R/config.R, lines 95–153 · score 0.51 · arcuate nucleus, GHRH, SST, TIDA, KNDy, CRABP1

Paper

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The authors' code

R · 200 lines · 8.5 KB · no license · 3 matches

  1. # =============================================================================
  2. # config.R -- all paths and analysis parameters in one place.
  3. #
  4. # This is the ONLY file that should need editing to re-run the pipeline on a
  5. # different machine. Nothing below hard-codes a path.
  6. # =============================================================================
  8. # --- Root directories --------------------------------------------------------
  9. # Override any of these with an environment variable of the same name if you
  10. # prefer not to edit the file (e.g. Sys.setenv(MBH_DATA = "/data/MBH")).
  12. PROJECT_ROOT <- Sys.getenv("MBH_PROJECT", unset = getwd())
  13. DATA_ROOT <- Sys.getenv("MBH_DATA", unset = file.path(PROJECT_ROOT, "data"))
  14. RESULTS_ROOT <- Sys.getenv("MBH_RESULTS", unset = file.path(PROJECT_ROOT, "results"))
  17. # --- Raw Xenium input --------------------------------------------------------
  18. # One entry per Xenium run. `slide` is the batch covariate used by DESeq2;
  19. # `prefix` is prepended to cell barcodes to keep them unique after merging.
  20. XENIUM_RUNS <- list(
  21. list(id = "anterior.1", prefix = "a1", slide = "slide.1",
  22. dir = file.path(DATA_ROOT, "anterior01_0043730"),
  23. roi = "anterior_01_coordinates.csv"),
  24. list(id = "anterior.2", prefix = "a2", slide = "slide.2",
  25. dir = file.path(DATA_ROOT, "anterior02_0043730"),
  26. roi = "anterior_02_coordinates.csv"),
  27. list(id = "anterior.3", prefix = "a3", slide = "slide.3",
  28. dir = file.path(DATA_ROOT, "anterior03_0043730"),
  29. roi = "anterior_03_coordinates.csv"),
  30. list(id = "posterior.1", prefix = "p1", slide = "slide.1",
  31. dir = file.path(DATA_ROOT, "posterior01_0050193"),
  32. roi = "posterior_01_coordinates.csv"),
  33. list(id = "posterior.2", prefix = "p2", slide = "slide.2",
  34. dir = file.path(DATA_ROOT, "posterior02_0050193"),
  35. roi = "posterior_02_coordinates.csv"),
  36. list(id = "posterior.3", prefix = "p3", slide = "slide.3",
  37. dir = file.path(DATA_ROOT, "posterior03_0050193"),
  38. roi = "posterior_03_coordinates.csv")
  39. )
  41. SEGMENTATION_METHOD <- "cell" # passed to LoadXenium(segmentations = )
  43. PATHS <- list(
  44. roi_coordinates = file.path(DATA_ROOT, "coordinates"),
  45. xenium_clusters = file.path(DATA_ROOT, "cluster_stats"),
  46. gene_panel = file.path(DATA_ROOT, "reference", "XeniumPrimeMouse5Kpan_tissue_pathways_metadata.csv"),
  47. hypomap = file.path(DATA_ROOT, "reference", "hypomap.RDS"),
  49. # Serialised Seurat objects, one per pipeline stage.
  50. obj_raw = file.path(DATA_ROOT, "objects", "01_arcuate_raw.RDS"),
  51. obj_normalized = file.path(DATA_ROOT, "objects", "02_arcuate_sct.RDS"),
  52. obj_labelled = file.path(DATA_ROOT, "objects", "03_arcuate_labelled.RDS"),
  53. obj_neurons = file.path(DATA_ROOT, "objects", "04_arcuate_neurons.RDS"),
  54. singleR_results = file.path(DATA_ROOT, "objects", "03_singleR_results.RDS"),
  55. singleR_training = file.path(DATA_ROOT, "objects", "03_singleR_training.RDS"),
  57. # Figure / table output, one directory per pipeline stage.
  58. out_qc = file.path(RESULTS_ROOT, "02_qc_normalization"),
  59. out_labels = file.path(RESULTS_ROOT, "03_cell_assignment"),
  60. out_neurons = file.path(RESULTS_ROOT, "04_neuron_analysis"),
  61. out_deseq2 = file.path(RESULTS_ROOT, "05_deseq2")
  62. )
  64. # --- Metadata schema ---------------------------------------------------------
  65. # ROI labels exported from Xenium Explorer encode condition, animal, and
  66. # anterior/posterior position, e.g. "NP_AL_02_POS_A".
  67. # <pregnancy>_<feeding>_<animal>_<ANT|POS>_<A|B hemisphere>
  68. META <- list(
  69. condition_col = "condition",
  70. sample_col = "tissue.number", # one animal = one biological replicate
  71. batch_col = "tissue.apaxis", # anterior vs posterior section
  72. slide_col = "slide.id", # physical slide, alternative batch term
  74. # Regex -> condition label. PregnantFM (pair-fed) is loaded but dropped in 02.
  75. condition_map = c(
  76. "^NP_AL_" = "AdLib",
  77. "^NP_F_" = "Fasted",
  78. "^P_AL_" = "Pregnant",
  79. "^P_FM_" = "PregnantFM"
  80. ),
  81. # Offset added to the animal number so IDs stay unique across conditions.
  82. animal_offset = c(AdLib = 0, Fasted = 5, Pregnant = 10, PregnantFM = 15),
  84. conditions_kept = c("AdLib", "Fasted", "Pregnant"),
  85. control_group = "AdLib"
  86. )
  88. # --- QC thresholds (pipeline 02) --------------------------------------------
  89. QC <- list(
  90. min_counts_per_cell = 50, # hard floor on transcripts per cell
  91. min_area_per_cell = 15, # hard floor on segmented area, um^2
  92. knee_divisor = 3 # keep cells above (count-per-area knee / this)
  93. )
  95. # --- Clustering (pipelines 02-04) -------------------------------------------
  96. CLUSTERING <- list(
  97. pca_dims = 1:30,
  98. whole_tissue_resolution = 0.4,
  99. neuron_resolution = 0.25,
  100. neuron_resolution_sweep = c(0.1, 0.25, 0.4, 0.5, 0.75, 1, 1.5),
  101. # SCTransform for the neuron subset regresses condition so that clusters
  102. # reflect cell identity rather than treatment (see Methods).
  103. neuron_vars_to_regress = "condition"
  104. )
  106. # Clusters removed as non-arcuate before the final round of labelling, and
  107. # clusters given a manual label because HypoMap does not represent them.
  108. # NOTE: these indices refer to the resolution set above and are stable only for
  109. # a fixed random seed -- see 03_cell_type_assignment.R.
  110. MANUAL_LABELS <- list(
  111. drop = c("VMH contaminants", "Pituitary Stalk", "Retrochiasmatic Area or Oxtocin Axons"),
  112. pass1 = c("2" = "VMH contaminants", "15" = "Microglia",
  113. "16" = "Pituitary Stalk", "19" = "Retrochiasmatic Area or Oxtocin Axons"),
  114. pass2 = c("6" = "Endothelial", "11" = "VLMCs", "14" = "VLMCs", "15" = "Microglia",
  115. "17" = "Pericyte")
  116. )
  118. NEURON_CLUSTER_NAMES <- c(
  119. "0" = "Mixed.GABAergic.Neurons",
  120. "1" = "AgRP",
  121. "2" = "Pomc",
  122. "3" = "Sst",
  123. "4" = "Ghrh",
  124. "5" = "Crabp1",
  125. "6" = "KNDy",
  126. "7" = "TIDA"
  127. )
  129. # --- DESeq2 (pipeline 05) ----------------------------------------------------
  130. # Sections excluded for histological damage or for being too anterior to
  131. # contain arcuate nucleus (see Methods, "Samples were excluded if...").
  132. OUTLIER_ROI <- list(
  133. # AdLib animal 2 posterior; Fasted animal 5 both sections
  134. Fasted = c("NP_AL_02_POS_A", "NP_AL_02_POS_B",
  135. "NP_F_05_POS_A", "NP_F_05_POS_B",
  136. "NP_F_05_ANT_A", "NP_F_05_ANT_B"),
  137. # AdLib animal 2 posterior; Pregnant animal 4 anterior
  138. Pregnant = c("NP_AL_02_POS_A", "NP_AL_02_POS_B",
  139. "P_AL_04_ANT_A", "P_AL_04_ANT_B")
  140. )
  142. DESEQ2 <- list(
  143. min_cells_per_pseudobulk = 15, # neurons
  144. min_cells_per_glial = 10, # glia are rarer per section
  145. alpha = 0.1,
  146. padj_cutoff = 0.05,
  147. shrink_type = "apeglm",
  148. # Gene filters. All zero for the published results: DESeq2's own independent
  149. # filtering is relied on instead. Kept explicit so reviewers can see this.
  150. min_count_per_gene = 0,
  151. min_samples_per_gene = 0,
  152. mean_count_threshold = 0
  153. )
  155. # The three models described in the Methods. Each is fit separately per cell
  156. # type; `contrast` is passed to results() and `coef` to lfcShrink().
  157. DESEQ2_MODELS <- list(
  158. fasted = list(
  159. label = "Fasted_vs_AdLib",
  160. conditions = c("AdLib", "Fasted"),
  161. design = ~ tissue.apaxis + condition,
  162. contrast = c("condition", "Fasted", "AdLib"),
  163. coef = "condition_Fasted_vs_AdLib",
  164. outliers = OUTLIER_ROI$Fasted,
  165. colour = "forestgreen"
  166. ),
  167. pregnant = list(
  168. label = "Pregnant_vs_AdLib",
  169. conditions = c("AdLib", "Pregnant"),
  170. design = ~ tissue.apaxis + condition,
  171. contrast = c("condition", "Pregnant", "AdLib"),
  172. coef = "condition_Pregnant_vs_AdLib",
  173. outliers = OUTLIER_ROI$Pregnant,
  174. colour = "dodgerblue"
  175. ),
  176. # Joint model: used for rlog-based QC (correlation heatmaps, PCA, gene
  177. # loadings) rather than for reported DGE, because the shared dispersion trend
  178. # shrinks the smaller fasting effect. See Methods, "DESeq2 Modeling".
  179. joint = list(
  180. label = "Joint_AllConditions",
  181. conditions = c("AdLib", "Fasted", "Pregnant"),
  182. design = ~ tissue.apaxis + condition,
  183. contrast = NULL,
  184. coef = NULL,
  185. outliers = unique(unlist(OUTLIER_ROI)),
  186. colour = "grey40"
  187. )
  188. )
  190. CONTROL_COLOUR <- "tomato"
  192. CONDITION_COLOURS <- c(AdLib = CONTROL_COLOUR,
  193. Fasted = DESEQ2_MODELS$fasted$colour,
  194. Pregnant = DESEQ2_MODELS$pregnant$colour)
  197. # --- Session -----------------------------------------------------------------
  198. RANDOM_SEED <- 42
  199. options(future.globals.maxSize = 40 * 1024^3) # 40 GB; SCTransform is greedy
  200. options(expressions = 5e5)

config.R at commit 805e1d6, no license · at the source

Overview

Authors: Ingrid Camila Possa-Paranhos1, Kerem Catalbas2, Samuel Congdon1, Dajin Cho2, Tanya Pattnaik1, Christina Nelson1, Aarav Pathak1, Vraj Patel1, Patrick Sweeney1,2
ORCID iDs: Kerem Catalbas, Dajin Cho, Tanya Pattnaik, Aarav Pathak, Vraj Patel, Patrick Sweeney
  1. Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign,Urbana, IL USA
  2. Neuroscience Program, University of Illinois Urbana-Champaign,Urbana, IL USA
Institutions: University of Illinois Urbana-Champaign (United States)
Journal: Nature communications, volume 17, issue 1, article 8751
Dates: received 25 August 2025; accepted 6 July 2026; published online 17 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75650-0 · PMID 42469283 · PMCID PMC13493881 · OpenAlex W7169549188
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Hypothalamus, Neural circuits, Feeding behaviour
MeSH: Arcuate Nucleus of Hypothalamus*, Eating*, Feeding Behavior*, Melanocortins*, Pro-Opiomelanocortin*, Agouti-Related Protein, Animals, Energy Metabolism, Female, Hyperphagia, Mice, Mice, Inbred C57BL, Neurons, Pregnancy, Transcriptome (* major topic)
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (R01HD113522)
Citations: not cited yet (Europe PMC); 109 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

SweeneyLab-UIUC/NCOMMS-25-67317A

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 805e1d6d6947fa5562fe5bf4f4a2ea7e59283fcf, 17 September 2026
Languages: R (16)
Size: 22 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (renv.lock)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Seurat (10 files), tidyverse (8 files), ggplot2 (4 files), patchwork (3 files), ComplexHeatmap (2 files), DESeq2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/SweeneyLab-UIUC/NCOMMS-25-67317A

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75650-0.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 12 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75650-0.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 15 MeSH terms, 1 funder, 103 references.

Cite

This paper

Possa-Paranhos, I. C., Catalbas, K., Congdon, S., Cho, D., Pattnaik, T., Nelson, C., Pathak, A., Patel, V., & Sweeney, P. (2026). Reduced melanocortin tone modulates feeding during pregnancy in mice. Nature communications, 17(1), 8751. https://doi.org/10.1038/s41467-026-75650-0

BibTeX

@article{possaparanhos2026reduced,
author = {Possa-Paranhos, Ingrid Camila and Catalbas, Kerem and Congdon, Samuel and Cho, Dajin and Pattnaik, Tanya and Nelson, Christina and Pathak, Aarav and Patel, Vraj and Sweeney, Patrick},
title = {{Reduced melanocortin tone modulates feeding during pregnancy in mice}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8751},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75650-0},
url = {https://doi.org/10.1038/s41467-026-75650-0},
pmid = {42469283},
pmcid = {PMC13493881}
}

RIS

TY - JOUR
AU - Possa-Paranhos, Ingrid Camila
AU - Catalbas, Kerem
AU - Congdon, Samuel
AU - Cho, Dajin
AU - Pattnaik, Tanya
AU - Nelson, Christina
AU - Pathak, Aarav
AU - Patel, Vraj
AU - Sweeney, Patrick
TI - Reduced melanocortin tone modulates feeding during pregnancy in mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/17
VL - 17
IS - 1
SP - 8751
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75650-0
UR - https://doi.org/10.1038/s41467-026-75650-0
LA - en
ER -

CSL-JSON

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"title": "Reduced melanocortin tone modulates feeding during pregnancy in mice",
"container-title": "Nature communications",
"author": [
{
"family": "Possa-Paranhos",
"given": "Ingrid Camila"
},
{
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{
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"DOI": "10.1038/s41467-026-75650-0",
"PMID": "42469283",
"PMCID": "PMC13493881",
"ISSN": "2041-1723",
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"issued": {
"date-parts": [
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17
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]
}
}

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