Feed API
Signals via API
Feed API access for integrations is available under Business plans. Public endpoints show a limited or redacted payload; Pro is for individual app access.
Public sample from the feed endpoint. The backend exposes read endpoints for signals, storylines, and briefings.
Public sample
GET /v1/feed/stories?limit=3&timeframe=24h&sort=momentum&tenant=biotech
{
"run_id": "d3636da7-2916-4324-a841-c925b032219a",
"timeframe": "24h",
"items": [
{
"type": "storyline",
"id": "b9217c24-619d-4149-bba7-7419e501e9f1",
"story_id": "b9217c24-619d-4149-bba7-7419e501e9f1",
"run_id": "d3636da7-2916-4324-a841-c925b032219a",
"snapshot_id": "8201c536-e4b9-4872-9929-064360efcb44",
"narrative_id": "145a8523-2057-40d4-b217-a05ce62ca559",
"first_seen": "2026-08-28T05:10:06.843756+00:00",
"last_seen": "2026-08-29T05:08:33.212161+00:00",
"first_seen_at": "2026-08-27T17:39:13.413407+00:00",
"last_seen_at": "2026-08-28T19:58:56.222710+00:00",
"status": "open",
"title": "G2T: Tissue Reconstruction from Gene Expression via Embedding-Distance Flow Matching",
"summary": "Single-cell RNA sequencing (scRNA-seq) profiles transcriptomes at high resolution but discards the spatial context of cells within a tissue -- information that is essential for studying intercellular mechanisms and tissue architecture. Spatial transcriptomics (ST) retains coordinates but, depending on the assay, trades this off against gene-panel breadth, spatial resolution, or cost. We present G2",
"top_tickers": [],
"why_now": [],
"metrics": {
"score_total": 0.721017,
"momentum_24h": 2,
"post_count": 2,
"unique_origin_publishers": 1,
"unique_publisher_types": 1,
"origin_share_top1": 1,
"amplifier_share_top1": null,
"duplicate_ratio": 0,
"evidence_score": 0.25,
"source_types_count": 1,
"unique_origin_domains": 1,
"unique_publishers": 1,
"quality_policy_version": 6,
"tenant_claim_coherence_policy_version": 2
},
"trend_status": "insufficient_history",
"trend_sparkline": "▁█▄",
"trend_points_n": 3,
"trend_window": {
"lookback_days": 14,
"max_points": 36,
"value_key": "score_total"
},
"maturity_label": "seed",
"maturity_score": 0.24902591666666668,
"top_sources": [
{
"label": "bioRxiv: FOCUS-3D: Robust, generalizable volumetric cell segmentation for three-dimensional fluorescence microscopy",
"source_type": "rss",
"evidence_class": "primary",
"provenance_role": "origin",
"publisher_subtype": "research"
}
],
"rank_story": 0.21600729150738326,
"p_score": 0.3333333333333333,
"p_mom": 0,
"recency": 0.9973624993628665,
"risk_penalty": 0.1,
"badges": [
"seed",
"insufficient_history",
"low_evidence"
],
"storyline_category": "narrative",
"storyline_category_reason": "multi_run_continuity",
"verified_update_issuer_count": 1,
"storyline_independent_origin_count": 1,
"storyline_continuity_points": 3,
"sources": [],
"llm_title": "AI tools advance tissue reconstruction and 3D cell analysis",
"llm_summary": "Two bioRxiv preprints describe AI-driven tools for studying tissue organization. FOCUS-3D performs volumetric cell segmentation in three-dimensional fluorescence microscopy and is used to analyze notochord morphogenesis in developing zebrafish. G2T reconstructs tissue coordinates from gene-expression data by predicting pairwise cell distances and applying multidimensional scaling.",
"llm_narrative_frame": "Recent bioRxiv work highlights complementary computational approaches to spatial biology. FOCUS-3D targets robust three-dimensional cell segmentation in fluorescence microscopy, while G2T infers tissue organization from gene-expression data by predicting cell-to-cell relationships and reconstructing coordinates.",
"llm_narrative_label": "AI methods for spatial biology",
"llm_narrative_type": "ongoing_storyline",
"llm_why_now": [
"Two related preprints were posted within the same news window, indicating active method development in AI-enabled spatial biology.",
"The work spans complementary data types: three-dimensional fluorescence microscopy and single-cell gene-expression profiles."
],
"llm_why_it_matters": [
"The studies target key spatial-biology challenges: extracting cells from volumetric images and recovering tissue structure from transcriptomic data.",
"Their approaches connect computational modeling with cellular organization, morphology and developmental transcriptional programs."
],
"llm_top_sources": [
{
"label": "FOCUS-3D preprint"
},
{
"label": "G2T preprint"
}
],
"entities": {
"projects": [
"FOCUS-3D",
"G2T"
],
"people": [
"Q. Zhang",
"Z. Mu",
"B. Liu",
"Y. Chi",
"D. Li",
"W. Wang",
"J.-Q. Ni",
"Y. Wan",
"L. Yu",
"J. Navajas Acedo",
"G. Yu",
"S. Birk"
]
},
"recurring_claims": [
{
"claim": "FOCUS-3D is presented as a generalizable framework for volumetric cell segmentation across species, tissues, fluorescent reporters and imaging modalities.",
"evidence_urls": [
"https://biorxiv.org/content/10.64898/2026.08.25.746907v1?rss=1"
]
},
{
"claim": "G2T uses gene-expression data to predict cell relationships and reconstruct two-dimensional tissue coordinates, addressing the loss of spatial context in single-cell RNA sequencing.",
"evidence_urls": [
"https://biorxiv.org/content/10.64898/2026.08.25.746917v1?rss=1"
]
}
],
"stance_map": [
{
"who": "FOCUS-3D authors",
"stance": "neutral",
"evidence_urls": [
"https://biorxiv.org/content/10.64898/2026.08.25.746907v1?rss=1"
]
},
{
"who": "G2T authors",
"stance": "neutral",
"evidence_urls": [
"https://biorxiv.org/content/10.64898/2026.08.25.746917v1?rss=1"
]
}
],
"quality_flags": {
"mixed_topic_risk": "low",
"promo_risk": "low",
"source_quality": "medium"
},
"editor_note": "Two fresh bioRxiv preprints apply AI to reconstructing and quantifying tissue organization from imaging and gene-expression data.",
"scope_tags": [
"biotech",
"genomics",
"research_and_development",
"spatial_biology",
"artificial_intelligence"
],
"scope_tags_raw": [
"biotech",
"genomics",
"research_and_development",
"spatial_biology",
"artificial_intelligence"
],
"llm_status": "accepted",
"llm_meta": {
"model": "gpt-5.6-luna",
"prompt_version": "enrich_v3",
"input_hash": "af2d5544625dfb8a980a54febd2dc9678778cd6c8f09045134c9e23f736fb4d2",
"updated_at": "2026-08-29T05:10:20.064136+00:00",
"llm_metadata": {
"env": "prod",
"host": "608ad6603356",
"stage": "signals.enrich",
"run_id": "d3636da7-2916-4324-a841-c925b032219a",
"tenant": "biotech",
"service": "api",
"pipeline": "pipeline_run",
"correlation_id": "145a8523-2057-40d4-b217-a05ce62ca559"
}
},
"display_title": "AI tools advance tissue reconstruction and 3D cell analysis",
"display_summary": "Two bioRxiv preprints describe AI-driven tools for studying tissue organization. FOCUS-3D performs volumetric cell segmentation in three-dimensional fluorescence microscopy and is used to analyze notochord morphogenesis in developing zebrafish.",
"display_tags": [
"biotech",
"genomics",
"research_and_development",
"spatial_biology",
"artificial_intelligence"
],
"tags": [
"biotech",
"genomics",
"research_and_development",
"spatial_biology",
"artificial_intelligence"
],
"cscope_tags": [
"biotech",
"genomics",
"research_and_development",
"spatial_biology",
"artificial_intelligence"
],
"display_why_now": [
"Two related preprints were posted within the same news window, indicating active method development in AI-enabled spatial biology.",
"The work spans complementary data types: three-dimensional fluorescence microscopy and single-cell gene-expression profiles."
],
"display_why_it_matters": [
"The studies target key spatial-biology challenges: extracting cells from volumetric images and recovering tissue structure from transcriptomic data.",
"Their approaches connect computational modeling with cellular organization, morphology and developmental transcriptional programs."
],
"why_now_display": [
"Two related preprints were posted within the same news window, indicating active method development in AI-enabled spatial biology.",
"The work spans complementary data types: three-dimensional fluorescence microscopy and single-cell gene-expression profiles."
],
"why_it_matters_display": [
"The studies target key spatial-biology challenges: extracting cells from volumetric images and recovering tissue structure from transcriptomic data.",
"Their approaches connect computational modeling with cellular organization, morphology and developmental transcriptional programs."
],
"show_why": true,
"sources_display": [
{
"label": "biorxiv.org: FOCUS-3D preprint"
}
],
"editor_status": "passed",
"fields_replaced": [],
"narrative_frame_display": "Recent bioRxiv work highlights complementary computational approaches to spatial biology. FOCUS-3D targets robust three-dimensional cell segmentation in fluorescence microscopy, while G2T infers tissue organization from gene-expression data by predicting cell-to-cell relationships and reconstructing coordinates.",
"display_rank_base_score": 2,
"display_rank_score": 1000000002,
"display_rank_boost": 1000000000,
"lane": "chatter",
"lane_reason": "maturity=seed",
"evidence_mix": {
"primary": 1,
"secondary": 0,
"specialist": 0,
"aggregator": 0,
"social": 0
},
"sourceStats": {
"qualityPolicyVersion": 6,
"evidenceDocumentCount": 2,
"uniqueItemCount": 2,
"uniquePublisherCount": 1,
"uniqueCanonicalOriginCount": 1,
"independentNonSocialCount": 1,
"primaryCount": 1,
"secondaryCount": 0,
"uniqueDomainsCount": 1,
"aggregatorCount": 0,
"socialCount": 0,
"sourceTypeDiversityCount": 1,
"topSourceShare": 1,
"passesTopSignalsGate": false,
"gateReason": "independentNonSocial=1; primary=1; secondary=0; documents=2; publishers=1; canonicalOrigins=1; topSourceShare=1.0; rule=>=2 unique canonical primary/secondary publishers; aggregators/social excluded"
},
"evidence_meta": {
"post_count": 2,
"unique_origin_publishers": 1,
"source_types_count": 1,
"origin_share_top1": 1
},
"gate": {
"passesTopSignalsGate": false,
"gateReason": "independentNonSocial=1; primary=1; secondary=0; documents=2; publishers=1; canonicalOrigins=1; topSourceShare=1.0; rule=>=2 unique canonical primary/secondary publishers; aggregators/social excluded"
},
"tenant_claim_coherence": {
"tenant": "biotech",
"status": "not_applicable",
"claim_scope": "not_applicable",
"reason_codes": [],
"public_anchors": {},
"evidence_anchors": {},
"debug": {
"evidence_record_count": 2,
"matching_record_count": 0,
"independent_support_count": 0,
"conflicting_record_count": 0
}
},
"evidence_quality_flag": "weak"
}
],
"next_cursor": "eyJ2IjoxLCJydW5faWQiOiJkMzYzNmRhNy0yOTE2LTQzMjQtYTg0MS1jOTI1YjAzMjIxOWEiLCJzb3J0IjoibW9tZW50dW0iLCJrIjpbMi4wLDAuNzIxMDE3LCJiOTIxN2MyNC02MTlkLTQxNDktYmJhNy03NDE5ZTUwMWU5ZjEiXX0",
"disclaimer": "No investment advice. Research signals and sources only. EarlyNarratives provides informational signals derived from public sources. It does not provide financial, legal, or tax advice."
}Capabilities
- Signals and storylines feed endpoints with filtering and rate limits
- Briefing delivery endpoints for integrations
- Evidence link payloads for auditability
Integrate in your workflow
- Route top stories into Slack or Teams for morning and evening desk updates.
- Sync storyline evidence into Notion, Airtable, or internal research trackers.
- Feed metrics into BI dashboards for momentum, concentration, and source mix monitoring.
Quick start endpoints: /v1/feed/stories, /v1/signals, /v1/storylines/search, /v1/briefings/latest.
For product access, see Pricing.