{
  "abstract": "BARseq enables spatially resolved gene-expression profiling across intact tissue, but its sensitivity depends on accurate detection and decoding of dense fluorescent rolonies. Existing spot-calling approaches can be conservative where signals overlap, while supervised deep learning is limited by the lack of reliable manual ground truth in real tissue images. Here we present PhySeqNet, a physics-informed deep learning framework for annotation-free fluorescent foci detection in BARseq. PhySeqNet trains U-Net detectors entirely on semi-synthetic images generated from an instrument-matched optical model combined with real sample-free experimental noise, and is integrated into pyBARseq, a modular Python pipeline for BARseq analysis. PhySeqNet achieved high detection performance on held-out simulated data across wavelength bands. On real tissue images, where ground truth is unavailable, we evaluated detections by forward-model reconstruction: PhySeqNet with conservative barcode-aware non-maxima suppression (NMSB) achieved higher reconstruction fidelity (NCC and MS-SSIM) than the BarDensr single-shot estimator, tuned classical spot detectors, and a pretrained deep-learning detector. In the analyzed brain section, PhySeqNet with NMSB yielded a median of 51 counts per cell versus 47 for BarDensr, with a similar number of genes detected per cell. The recovered transcripts produced spatial expression patterns qualitatively consistent with Allen MERFISH reference data and preserved cell-type composition after scANVI label transfer. These results show that physics-informed simulation can train practical, annotation-free detectors that improve BARseq sensitivity without disrupting downstream biological interpretation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13779",
  "author_total": 7,
  "authors": [
    {
      "name": "Neha Goswami"
    },
    {
      "name": "John Hover"
    },
    {
      "name": "Renae Galluccio"
    },
    {
      "name": "Yichen Wu"
    },
    {
      "name": "Huiqing Zhan"
    },
    {
      "name": "Xiaoyin Chen"
    },
    {
      "name": "Anthony M. Zador"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7m4101e8jctf3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13779",
  "fulltext_url": null,
  "id": "7m4101e8jctf3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13779",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221538818",
  "pdf_url": "https://arxiv.org/pdf/2610.13779",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7m4101e8jctf3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:36Z",
      "native_id": "2610.13779",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13779"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "PhySeqNet: physics-informed fluorescent foci detection for BARseq spatial transcriptomics",
  "title_text": "PhySeqNet: physics-informed fluorescent foci detection for BARseq spatial transcriptomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:41Z",
  "url": "https://research.draconitic.com/paper/7m4101e8jctf3",
  "venue": "arXiv",
  "versions": []
}