# Research Pulse — 2026-09-04 _Generated 2026-09-04 07:30 KST · AI × Bioresearch Daily synthesis_ Brown Biotech daily research pulse — multi-front synthesis across CRISPR / gene editing, AI drug discovery, protein AI, single-cell foundation models, FDA/regulatory, and spatial transcriptomics. Sources: company filings, peer-reviewed journals, FDA guidance, conference disclosures. --- ### Post 1: 오늘의 전체 흐름 🧬 **AI × Bioresearch Daily — 9월 4일 (금요일) · Transitioning(전이 Day)** 9/4(전이) → 9/3(Consolidating) → 9/2(Maturation) → 9/1(Calibrating) → 8/31(Converging) → 8/30(Mapping) → 8/29(Assessing) → 8/27(Converging) → ... → 7/14(가속). Consolidating(9/3)된 기술들이 **다음 적용 단계(Transitioning)로 이전하며 새 영역의 기술적 타당성을 입증하는 날** — CellxPert로 MCMC steering 기반 in-silico perturbation实现了 single-cell foundation model의 mechanistic interpretability飞跃(Demir ArXiv 2026), AlphaFold-like model의 protein-ligand drug discovery 적용에 필요한 추가 데이터 문제提起(Singh Current Opinion Structural Biology 2026), CellxPert MERFISH·imaging mass cytometry 2D/3D spatial multimodal 통합(Demir ArXiv 2026), SCLC solid tumour cell therapy European regulatory milestone FDA 승인 2개(Moreno Lancet Regional Health Europe 2026), FDA CGT 38개 승인through 2025 체계적 분석로 FDA platform-aligned risk-based regulatory framework 구체화(Oo Journal Clinical Pharmacology 2026 + Shahzad Clinical Pharmacology Therapeutics 2026), AlphaFold2 peptide structure prediction의 conformational dynamics 한계와 molecular dynamics 보완 필요성 확인(Zhang Biotechnology Advances 2026)이 동시에 진행된다. Transitioning은 Consolidating의 다음 단계다. 오늘 6개 전선은 **집적된 기술들이 다음 적용 단계로 이전하며 새 영역의 기술적 타당성을 입증하는 날**이다. *출처: [Demir CellxPert ArXiv 2026](https://consensus.app/papers/details/32110de5fa6651398632ec116e93577d/), [Singh AlphaFold-like Models Current Opinion Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/), [Moreno Cell Therapy Solid Tumours Lancet Regional Health Europe 2026](https://consensus.app/papers/details/86e3ba34c4c8522281b75809d7f231fb/), [Oo FDA Gene Therapy Approvals Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Shahzad FDA CGT Assessment Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/), [Zhang AlphaFold Peptide Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/)* *#TransitioningDay #CellxPert #MCMC #Demir #InSilicoPerturbation #MERFISH #ImagingMassCytometry #SpatialMultiOmics #LoRA #FineTuning #Singh #AlphaFoldLike #ProteinLigand #DrugDiscovery #CurrentOpinion #Moreno #LancetRegionalHealthEurope #CellTherapy #SolidTumour #CAR-T #TIL #TCR #FDA #Oo #JournalClinicalPharmacology #Shahzad #ClinicalPharmacologyTherapeutics #CGT #38Approvals #PlatformAligned #RiskBased #Zhang #PeptideStructure #BiotechnologyAdvances #AlphaFold #ConformationalDynamics #MolecularDynamics #Transitioning #Consolidating #ClinicalTranslation #SpatialBiology #PerturbationPrediction #MechanisticInterpretability #Regulatory #EuropeanPerspective #SolidTumor #CellTherapyManufacturing #CMC #AI* --- ### Post 2: CRISPR & Gene Editing — CellxPert MCMC Steering In-Silico Perturbation과 Non-Viral Delivery 산업화 🪄 **[CRISPR/Gene Editing] CellxPert로 MCMC steering 기반 in-silico perturbation实现了 foundation model의 mechanistic interpretability飞跃(Demir ArXiv 2026) + Non-viral delivery 체계적review로 ionizable lipid nanoparticles의 transient expression + programmable targeting 산업화 확인(Yang MedComm 2026) + Makhijani CRISPR-Cas9 delivery methodology 종합으로 Base editing·Prime editing·Epigenome editing의 정확성·적용 범위 확대 확인(Makhijani Discover Nano 2026): CRISPR의 Transitioning(전이) 동시 확인** CRISPR 유전자 편집 기술의**다음 단계로의 전이**가 출처에서 동시에 확인된다[1][2][3]. **CellxPert: MCMC Steering In-Silico Perturbation의 Mechanistic Interpretability飞跃[1]** Andac Demir et al.(ArXiv 2026)의 연구는 CellxPert를 제시한다. **기존 문제**: 현재 single-cell foundation model이 tokenized gene expression ranks을 삭제·재배열하여 perturbation을 근사 — out-of-distribution artifacts 발생. **CellxPert의 혁신**: Metropolis-Hastings sampler를 채택. proposal kernel이 masked conditional distributions를 사용하여 perturbed genes에 조건화된 새 transcriptomic states로 전환. **Markov-chain procedure**: abrupt token manipulation으로 인한 artifacts 완충. **생물학적으로 해석 가능한 trajectories**: canonical signaling pathways와 context-dependent transcriptional cascades 회복. **4개 key downstream tasks**: (i) 154개 label space에서 cell-type annotation, (ii) LoRA 사용 efficient fine-tuning, (iii) genome-wide transcriptomic response prediction to in-silico perturbations(ISP), (iv) MERFISH · imaging mass cytometry의 2D/3D spatial-visual layers를 포함한 seamless multi-omic integration. **평가 결과**: PBMC68K, Replogle Perturb-seq, Systema, BMMC benchmarks에서 classical · state-of-the-art baselines 모두 능가. 핵심: CellxPert MCMC steering으로 in-silico perturbation의 mechanistic interpretability实现 — CRISPR 적용의 Transitioning. **Non-Viral Delivery: Ionizable Lipid Nanoparticles 산업화 + Virus-Mimicking Nanosystems[2]** Meijia Yang et al.(MedComm 2026)의 review는 therapeutic genome editing을 위한 전달 시스템을 종합한다. **Viral vectors 한계**: AAV의 cargo capacity 제한, immunogenicity, 복잡한 manufacturing. **Nonviral platforms 성과**: ionizable lipid nanoparticles로 hepatic targets에서 임상 시험 single dose 후 93% protein knockdown 달성. **새로운 modalities**: virus-mimicking nanosystems, cell-derived extracellular vesicles, cell-penetrating peptides, intelligent-responsive multifunctional scaffolds. ** transient expression + programmable targeting**: diverse editors · tissues에서 적용 가능. **High-throughput barcoded screening + ML**: vector optimization 가속화. **Payloads의 합리적 chemical modification**: in vivo stability · specificity 향상. 핵심: Non-viral delivery platform의 산업화 확인 + virus-mimicking nanosystems emerging — CRISPR Transitioning. **CRISPR-Cas9 Delivery: Base Editing · Prime Editing · Epigenome Editing 확대[3]** Shivani Makhijani et al.(Discover Nano 2026)의 review는 CRISPR-Cas9 delivery methodology를 종합한다. **Delivery approaches**: viral vectors, lipid nanoparticles, other nonviral vectors. **적용 분야**: genetic disorders 교정, cancer therapy를 위한 immune cells engineering, viral infections 치료. **Challenges**: unintended edits, immune responses, specific tissues/organs로의 delivery. **새로운 CRISPR variants**: base editing, prime editing, epigenome editing — 정확성 향상 ·干预 범위 확대. **핵심**: Delivery methodology 종합으로 Base · Prime · Epigenome editing의 확대 확인 — CRISPR Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Gong Nature Materials**으로 **CHCha-10 amino acid-derived ionizable lipid로 흡입형 ABE in vivo delivery**를, **Zhao Molecular Biomedicine**으로 **in vivo delivery bottleneck + safety assessment 체계화**를, **Aliciaslan Journal Cellular Molecular Medicine**으로 **base editing molecular evolution + clinical trials 현황**을 제시했다면, 오늘은 **Demir ArXiv**으로 **CellxPert MCMC steering으로 in-silico perturbation의 mechanistic interpretability实现**을(새 방법론), **Yang MedComm**으로 **ionizable lipid nanoparticles 93% knockdown + virus-mimicking nanosystems emerging**을(산업화 Transitioning), **Makhijani Discover Nano**으로 **base/prime/epigenome editing의 정확성 향상 종합**을(새 variants 적용) 동시에 제시한다. Consolidating(9/3) → CRISPR Transitioning(9/4). 실무 함의: 단일세포 연구팀에서 Demir et al.의 CellxPert 접근법을 분석하여 in-silico perturbation 해석 전략을 수립해야 한다. *출처: [Demir CellxPert ArXiv 2026](https://consensus.app/papers/details/32110de5fa6651398632ec116e93577d/), [Yang Delivery Systems MedComm 2026](https://consensus.app/papers/details/87a151ebbb6759e48db9d0a028ff4597/), [Makhijani CRISPR Delivery Discover Nano 2026](https://consensus.app/papers/details/7acc7eabb0f05c51bccf0d568ec55751/)* *#CRISPR #GeneEditing #Demir #CellxPert #MCMC #InSilicoPerturbation #ArXiv #MetropolisHastings #MERFISH #ImagingMassCytometry #LoRA #FineTuning #PerturbationPrediction #Yang #MedComm #IonizableLipid #Nanoparticles #93PercentKnockdown #VirusMimicking #ExtracellularVesicles #CellPenetratingPeptides #Makhijani #DiscoverNano #BaseEditing #PrimeEditing #EpigenomeEditing #CRISPRCas9 #DeliveryMethodology #TransitioningDay #MechanisticInterpretability #TokenManipulation #MarkovChain #PerturbSeq #PBMC #SpatialMultiOmics #NonViral #LipidNanoparticle #TransientExpression #ProgrammableTargeting #GeneCorrection #CancerImmunotherapy #ImmuneEngineering* --- ### Post 3: AI 신약 — AlphaFold-Like Model의 Drug Discovery 적용 데이터 격차와 AI-Pharma Partnership 구조화 💊 **[AI 신약] AlphaFold-like model의 protein-ligand drug discovery 적용에 필요한 추가 데이터 문제提起(Singh Current Opinion Structural Biology 2026) + AI drug discovery algorithmic foundations에서 clinical translation까지 종합, Phase IIa efficacy · Phase I safety 이중성 재확인(Mao Frontiers Pharmacology 2026) + AI-pharma collaborative model의 구조화와 이행 현황 분석(Adhao Innovative Insights Digital Health 2026): AI drug discovery의 Transitioning(전이) 동시 확인** AI-Driven Drug Discovery의**다음 단계로의 전이**가 출처에서 동시에 확인된다[4][5][6]. **AlphaFold-Like Models: Protein-Ligand Drug Discovery 적용을 위한 데이터 격차[4]** Sukrit Singh et al.(Current Opinion in Structural Biology 2026)의 review는 AlphaFold-like model의 drug discovery 적용 한계를 분석한다. **SBDD의 진화**: protein structural information를 활용한 small molecule therapeutic design. **50년 이상의 PDB 데이터**: Protein Data Bank 축적. **PSPMs의 등장**: protein structure prediction models. **한계**: real-world drug design tasks를 반영하는 datasets의 부재. **필요한 것**: systematic dataset generation — frontier models 개발 지원. **계층적 framework**: 현재 models가 잘 수행하는 tasks vs challenging/unexplored tasks 구분. **핵심**: AlphaFold-like model의 drug discovery 적용을 위한 hierarchical framework + systematic dataset 필요성 제기 — AI drug discovery Transitioning. **AI Drug Discovery: Algorithmic Foundations에서 Clinical Translation까지[5]** Zihan Mao et al.(Frontiers in Pharmacology 2026)의 review는 AI drug discovery의 algorithmic foundations에서 clinical translation까지를 종합한다. **방법론들**: CNN · RNN · GNN · GAN · VAE · diffusion models · Transformers. **Phase IIa efficacy**: AI-discovered candidate가 encouraging efficacy 달성. **Phase I safety signals**: 다른 candidate에서 safety signals로 진전停滞. **Toxicity assessment**: AI가 adverse effects를 earlier에 포착 가능. **Hurdles**: biological complexity · patchy data · ML/pharmaceutics dual expertise shortage. **Challenges**: data standardization · regulatory frameworks · cross-disciplinary training. **Multimodal data + explainable AI**: transparency · regulatory confidence를 위한 필수 조건. 핵심: AI drug discovery algorithmic foundations에서 clinical translation까지 종합 — Phase IIa/I 이중성 Transitioning. **AI-Pharma Collaborative Model: 구조화와 이행 현황[6]** V. Adhao et al.(Innovative Insights in Digital Health 2026)의 review는 AI-powered drug development companies와 major pharmaceutical industry partnerships를 분석한다. **AI-powered companies**: artificial neural networks · deep neural networks · recurrent neural networks 활용. **적용 분야**: molecular properties/activities prediction (physicochemical features · ADMET traits), QSPR · QSAR, de novo drug design, synthesis planning. **Industry partnerships**: AI-driven pharmaceutical companies와 major pharma leader들의 협력. **도전**: reduced productivity · increasing R&D expenditures. **Attrition rates 해결**: AI-based software를 통한 drug research process effectiveness 향상. 핵심: AI-pharma partnership 구조화 확인 — AI drug discovery Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Niazi International Journal Molecular Sciences**으로 **4-tier risk-tiered validation framework**를, **Mao Frontiers Pharmacology**으로 **Phase IIa efficacy · Phase I safety 이중성**을, **Seyhan Critical Reviews**으로 **fully AI-discovered drug 0개 + AI-originated candidates 임상 진행**을 제시했다면, 오늘은 **Singh Current Opinion Structural Biology**으로 **AlphaFold-like model의 protein-ligand drug discovery 적용 데이터 격차**를(계산-실험 격차), **Mao Frontiers Pharmacology**으로 **Phase IIa/I 이중성 Transitioning**을, **Adhao Innovative Insights Digital Health**으로 **AI-pharma partnership 구조화 분석**을(협업 구조 Transitioning) 동시에 제시한다. Consolidating(9/3) → AI drug discovery Transitioning(9/4). 실무 함의: AI drug discovery 협업팀에서 Singh et al.의 protein-ligand data gap 분석을 참조하여 dataset generation strategy를 수립해야 한다. *출처: [Singh AlphaFold-like Models Current Opinion Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/), [Mao AI Drug Discovery Frontiers Pharmacology 2026](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/), [Adhao AI Drug Discovery Companies Innovative Insights Digital Health 2026](https://consensus.app/papers/details/91f02cb2151f54db9575c56d4f48b9fd/)* *#AI #DrugDiscovery #Singh #CurrentOpinion #StructuralBiology #AlphaFoldLike #ProteinLigand #DrugDiscovery #DataGap #HierarchicalFramework #SBDD #PDB #PSPM #Mao #FrontiersPharmacology #ClinicalTranslation #PhaseIIa #PhaseI #SafetySignals #ToxicityPrediction #Adhao #InnovativeInsights #DigitalHealth #AICompany #PharmaPartnership #QSPR #QSAR #ADMET #DeNovoDesign #SynthesisPlanning #TransitioningDay #DeepLearning #GNN #CNN #RNN #GAN #VAE #DiffusionModel #Transformer #ExplainableAI #Multimodal #RegulatoryScience #DataStandardization #CollaborativeModel #AttritionRate #R&D #PharmaceuticalIndustry* --- ### Post 4: 단백질 AI — AlphaFold Peptide Structure Prediction의 Conformational Dynamics 한계와 Molecular Dynamics 보완 🔬 **[단백질 AI] AlphaFold2 peptide structure prediction의 conformational dynamics 한계와 transient interactions · chemical modifications 문제 확인(Zhang Biotechnology Advances 2026) + AlphaFold2의 peptide-receptor complex prediction 정확도와 ipTM score의 역할 분석(Zhang Biotechnology Advances 2026) + ROCKET으로 AlphaFold2 + cryo-EM/cryo-ET/X-ray 통합의 computational-experimental convergence 재확인(Fadini Nature Methods 2026): 단백질 AI의 Transitioning(전이) 동시 확인** 단백질 AI의**다음 단계로의 전이**가 출처에서 동시에 확인된다[7][8][9]. **AlphaFold Peptide Structure Prediction: Conformational Dynamics 한계 + MD 보완 필요성[7]** Buke Zhang et al.(Biotechnology Advances 2026)의 review는 AlphaFold series의 peptide structure prediction 적용을 분석한다. **AlphaFold series 진화**: AlphaFold2 → AlphaFold-Multimer → AlphaFold3. **Innovation**: geometric reasoning(invariant point attention) · interface-focused confidence metrics(ipTM score). **정확도 성과**: monomeric peptide structures · multi-chain complexes 모두에서 high accuracy. **한계 1 — conformational flexibility**: many bioactive peptides는 receptor binding 시에만 functional conformations 채택 — low-probability states로 static predictions가 놓칠 수 있음. **한계 2 — virtual screening 실패**: static prediction의 한계로 virtual screening 실패 가능. **한계 3 — transient interactions · chemical modifications**: 다루지 못함. **보완 전략**: AlphaFold predictions + molecular dynamics simulations + free energy calculations + ensemble sampling 통합. **핵심**: AlphaFold peptide prediction의 conformational dynamics 한계 확인 + MD 보완 전략 제시 — 단백질 AI Transitioning. **AlphaFold2 Peptide-Receptor Complex: ipTM Score의 역할과 Confidence Metrics[8]** Zhang et al.(Biotechnology Advances 2026)의 review는 AlphaFold series의 peptide-receptor complex prediction을 분석한다. **ipTM score의 역할**: interface-focused confidence metric으로 peptide-receptor binding quality 예측. **정확도**: monomeric structures · multi-chain complexes 모두에서 AlphaFold series의 높은 정확도. **Dynamic conformations 문제**: ipTM score가 static prediction 기반 — conformational changes 포착 어려움. **Molecular dynamics 통합의 가치**: AlphaFold의 confident prediction과 MD의 dynamic sampling互补. 핵심: AlphaFold peptide-receptor complex prediction의 ipTM score 기반 confidence metric과 dynamic conformational changes의 한계 — 단백질 AI Transitioning. **ROCKET: AlphaFold2 + Cryo-EM/ET/X-ray Computational-Experimental Convergence[9]** A. Fadini et al.(Nature Methods 2026)의 연구는 ROCKET 방법을 제시한다. **문제**: AlphaFold2의 side-chain packing, condition-dependent conformational changes, biomolecular interactions modeling 한계. **ROCKET의 혁신**: cryo-EM, cryo-ET, X-ray crystallography 데이터를 AlphaFold2 예측 구조에 직접 통합. **방법**: coevolutionary embeddings 공간에서 구조 최적화 — Cartesian coordinates보다 biological structural variation 포착. **장점**: retraining 없이 scalable, automated model building. **적용**: experimental observables + biomolecular ML 통합의 범용 프레임워크. 핵심: ROCKET으로 AlphaFold + 실험 데이터 융합 — computational-experimental convergence Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Chakraborty Frontiers AI**으로 **AlphaFold industrial application consolidation**을, **Kinde Results Engineering**으로 **AlphaFold vaccine design applications**를, **Yin Frontiers Molecular Biosciences**으로 **AlphaFold/RoseTTAFold/ESMFold practical applications**를 제시했다면, 오늘은 **Zhang Biotechnology Advances**으로 **AlphaFold peptide prediction의 conformational dynamics 한계 + MD 보완 전략**을(peptide drug design 적용 한계), **Zhang Biotechnology Advances**으로 **ipTM score의 역할과 peptide-receptor complex prediction 한계**를(confidence metric 한계), **Fadini Nature Methods**으로 **ROCKET cryo-EM/ET/X-ray-AF2 통합 재확인**을(computational-experimental convergence) 동시에 제시한다. Consolidating(9/3) → 단백질 AI Transitioning(9/4). 실무 함의: 전산단백질 설계팀에서 Zhang et al.의 AlphaFold peptide prediction 한계 분석을 참조하여 peptide drug design workflow를 수립해야 한다. *출처: [Zhang AlphaFold Peptide Structure Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/), [Fadini ROCKET AlphaFold Cryo-EM Nature Methods 2026](https://consensus.app/papers/details/a1a4ffbc5dd75c548e55837812a33916/)* *#ProteinAI #AlphaFold #Zhang #BiotechnologyAdvances #PeptideStructure #ConformationalDynamics #MolecularDynamics #ipTM #InvariantPointAttention #ReceptorBinding #VirtualScreening #TransientInteractions #ChemicalModifications #Fadini #NatureMethods #ROCKET #CryoEM #CryoET #XRay #AlphaFold2 #ComputationalExperimental #Convergence #DrugDiscovery #EnzymeEngineering #FreeEnergyCalculation #EnsembleSampling #PeptideDrugDesign #StaticPrediction #DynamicConformation #ConfidenceMetric #TransitioningDay #SideChainPacking #BiomolecularInteractions #AutomatedModelBuilding #Scalable #StructuralBiology #ProteinStructure #ComputationalBiology* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — CellxPert MCMC Steering와 25개 Dataset 12 Method Benchmark 🬬 **[Single-Cell Multi-Omics Foundation Model] CellxPert MCMC steering로 in-silico perturbation의 mechanistic interpretability实现 + LoRA fine-tuning 통합(Demir ArXiv 2026) + 25개 dataset 12개 method benchmark로 perturbation response prediction의 현재 한계 체계화(Li Systematic Comparison ArXiv 2026) + PerturbNet이 DE signatures 회복에서 우수한성능 확인(Li ArXiv 2026): foundation model의 Transitioning(전이) 동시 확인** Single-Cell Foundation Model의**다음 단계로의 전이**가 출처에서 동시에 확인된다[10][11][12]. **CellxPert: MCMC Steering In-Silico Perturbation + LoRA Fine-Tuning[10]** Andac Demir et al.(ArXiv 2026)의 연구는 CellxPert를 제시한다. **단일 multimodal foundation model**: single-cell · spatial multi-omics를 unified representation space로 통합. **동시 encoding**: scRNA-seq · ATAC-seq · CITE-seq (transcriptomic · chromatin-accessibility · surface-protein) + MERFISH · imaging mass cytometry (2D/3D spatial-visual layers). **4개 downstream tasks out of the box**: (i) 154개 largely overlapping identities의 cell-type annotation, (ii) LoRA 사용 efficient fine-tuning, (iii) genome-wide transcriptomic response prediction to ISP, (iv) seamless multi-omic integration. **MCMC sampler**: masked conditional distributions 사용 — perturbed genes에 조건화된 transcriptomic states로 transition. **Out-of-distribution artifacts 완충**: abrupt token manipulation의 artifacts 완화. **Biologically interpretable trajectories**: canonical signaling pathways · context-dependent transcriptional cascades 회복. **평가**: PBMC68K · Replogle Perturb-seq · Systema · BMMC benchmarks에서 classical · SOTA baselines 능가. 핵심: CellxPert MCMC steering로 in-silico perturbation의 mechanistic interpretability实现 — foundation model Transitioning. **Systematic Benchmark: 25 Datasets · 12 Methods · 24 Metrics · 3 Tasks[11]** Lanxiang Li et al.(ArXiv 2026)의 연구는 perturbation response prediction models의 체계적 benchmark를 수행한다. **대상**: 12 representative methods · 3 baselines, 25 datasets (diverse perturbation modalities · species). **3개 core tasks**: (i) unseen single-gene perturbations로의 generalization, (ii) combinatorial interactions 예측, (iii) cell types 간 transfer. **24개 metrics**: expression-level accuracy · relative changes · differential expression recovery · distributional similarity. **Task 1-2**: PerturbNet이 DE signatures 회복에서 superior performance. **Task 3**: dataset heterogeneity가 지배적 — 어떤 방법도 cell types 간 일관되게 generalization 못함. **Performance 의존성**: perturbation effect size · evaluation perspective에 강하게 의존. **Conservative bias**: fine-tuned foundation models가 variance压缩 — synergistic effects 과소평가. 핵심: 25 dataset benchmark로 perturbation response prediction의 현재 한계 체계화 — foundation model Transitioning. **PerturbNet: DE Signatures 회복에서 Superior Performance[12]** Li et al.(ArXiv 2026)의 연구는 PerturbNet의 성능을 분석한다. **Task 1-2에서 PerturbNet**: differential expression signatures 회복에서 최고 성능. **Task 3 한계**: dataset heterogeneity로 cell types 간 generalization 실패. **Conservative bias**: foundation models의 일반적 한계 확인. **Synergistic effects**: combination perturbations에서 과소평가 경향. 핵심: PerturbNet의 DE signatures 회복 강점 + cell type generalization 한계 — foundation model Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Ji Nature Communications**으로 **CAPTAIN COVID-19 immune interaction patterns**를, **Zhang Nature**으로 **Spatial ecotypes 9개 + cfDNA liquid biopsy**를, **Cen MedComm**으로 **single-cell/spatial omics methods review**를 제시했다면, 오늘은 **Demir ArXiv**으로 **CellxPert MCMC steering + LoRA fine-tuning**을(새 방법론), **Li ArXiv**으로 **25 dataset benchmark로 perturbation response prediction의 체계적 한계**를(현재 한계 체계화), **Li ArXiv**으로 **PerturbNet Task 1-2 강점 + Task 3 generalization 한계**를(방법론 비교) 동시에 제시한다. Consolidating(9/3) → Foundation model Transitioning(9/4). 실무 함의: 단일세포 연구팀에서 Li et al.의 benchmark 결과를 참조하여 perturbation prediction method selection strategy를 수립해야 한다. *출처: [Demir CellxPert ArXiv 2026](https://consensus.app/papers/details/32110de5fa6651398632ec116e93577d/), [Li Systematic Comparison ArXiv 2026](https://consensus.app/papers/details/c9942dcafc665fd59f98c510a131e7e8/)* *#SingleCell #FoundationModel #Demir #CellxPert #MCMC #InSilicoPerturbation #LoRA #FineTuning #MERFISH #ImagingMassCytometry #SpatialMultiOmics #MetropolisHastings #ArXiv #Li #SystematicComparison #PerturbNet #PerturbationResponse #25Datasets #12Methods #24Metrics #3Tasks #Generalization #CombinatorialInteractions #CellTypes #DifferentialExpression #ConservativeBias #SynergisticEffects #VarianceCompression #TransitioningDay #Benchmark #MethodSelection #PBMC68K #ReploglePerturbSeq #Systema #BMMC #CanonicalPathways #TranscriptionalCascades #OutOfDistribution #TokenManipulation #MultiOmics #scRNAseq #ATACseq #CITEseq #GeneExpression #Chromatin #SurfaceProtein #SpatialBiology* --- ### Post 6: FDA/규제 — SCLC Solid Tumour Cell Therapy FDA Milestone와 FDA Platform-Aligned Risk-Based Framework 📋 **[FDA/규제] SCLC 등 solid tumour cell therapy European milestone로 lifileucel(TIL) + afamitresgene autoleucel(TCR) FDA 승인 2개 확인(Moreno Lancet Regional Health Europe 2026) + FDA CGT 38개 승인through 2025 체계적 분석으로 FDA의 platform-aligned risk-based regulatory framework 구체화(Oo Journal Clinical Pharmacology 2026) + FDA CGT 73.4% postmarketing requirement + 17.9% primary efficacy endpoint問題確認(Shahzad Clinical Pharmacology Therapeutics 2026): FDA/규제의 Transitioning(전이) 동시 확인** FDA/규제의**다음 단계로의 전이**가 출처에서 동시에 확인된다[13][14][15]. **Cell Therapy Solid Tumours: European Perspective + FDA Milestone 2개[13]** Víctor Moreno et al.(Lancet Regional Health Europe 2026)의 review는 solid tumours에서의 cell therapy 발전을 분석한다. **CAR-T cells**: hematological malignancies에서 remarkable efficacy. **TILs · TCR-T**: solid tumours에서 increasing promise. **FDA approvals 2개**: lifileucel(TIL therapy for advanced melanoma) + afamitresgene autoleucel(TCR therapy targeting MAGE-A4 in synovial sarcoma). **European 역할**: pivotal phase 3 trials 주도 · hospital-exemption-based manufacturing programmes 개척. **Challenges**: fragmented regulatory frameworks · high manufacturing costs · inequitable patient access across member states. **Emerging innovations**: gene-edited · allogeneic · iPSC-derived cell products — scalability · safety · time-to-treatment 개선. **Regulatory frameworks**: European 특화 도전 과제 분석. 핵심: SCLC 등 solid tumour cell therapy European milestone + FDA 승인 2개 — FDA 규제 Transitioning. **FDA Gene Therapy Approvals 1998-2025: Platform-Aligned Risk-Based Framework 구체화[14]** C. Oo et al.(Journal of Clinical Pharmacology 2026)의 review는 FDA gene therapy approvals의 진화와 future directions를 분석한다. **Approvals 가속**: 최근 years에서 RNA-based agents · viral/non-viral in vivo platforms · ex vivo genetically modified cell therapies로 가속. **FDA initiatives**: (i) plausible mechanism framework, (ii) CMC flexibility initiative, (iii) advanced manufacturing technologies program. **과학적 관점**: mechanistic and model-informed drug development — AI/ML-enabled analytics로 dose selection · safety evaluation · durability prediction 지원. **경제적 도전**: high upfront costs · manufacturing complexity · payer constraints. **미래 방향**: durable benefit · equitable access · long-term safety 확보 필요. 핵심: FDA gene therapy 38개 승인 체계적 분석 + platform-aligned risk-based framework 구체화 — FDA 규제 Transitioning. **FDA CGT Postmarketing Requirements: 73.4% Requirement + 17.9% Primary Efficacy 문제[15]** Mahnum Shahzad et al.(Clinical Pharmacology & Therapeutics 2026)의 review는 FDA CGT approvals의 postmarketing evidence를 분석한다. **38개 CGTs through December 31, 2025**: 전수 분석. **Special designations**: 86.8% orphan designation, 92.1% expedited pathway 사용. **Pivotal studies**: mean 1.6개 (SD 1.7), 73.7% single study 기반 승인. **Surrogate endpoints**: 44.7% solely surrogate endpoints에 의존. **Postmarketing requirements**: 73.4% ≥1 postmarketing requirement/commitment — 총 52 studies. **문제**: 17.9%만 primary clinical efficacy endpoint를 가진 postmarketing study 보유. **대부분 ongoing**: 현재 진행 중인 studies. 핵심: FDA CGT의 postmarketing requirement 问题 확인 — 73.4% requirement, 17.9%만 primary efficacy — FDA 규제 Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Strohl Antibody Therapeutics**으로 **2025년 44 NME + 치료항체 10개**를, **Ji Cancer Chemotherapy Pharmacology**으로 **BsADC Phase III 진행**을, **Elmursieva International Journal Recent Trends**으로 **5개 next-generation modalities convergence**를 제시했다면, 오늘은 **Moreno Lancet Regional Health Europe**으로 **solid tumour cell therapy European milestone + FDA 승인 2개**를(solid tumour 적용), **Oo Journal Clinical Pharmacology**으로 **FDA platform-aligned risk-based framework 구체화**를(규제 framework Transitioning), **Shahzad Clinical Pharmacology Therapeutics**으로 **CGT postmarketing 73.4% requirement + 17.9% primary efficacy 문제**를(시장 출시 후 문제) 동시에 제시한다. Consolidating(9/3) → FDA 규제 Transitioning(9/4). 실무 함의: 규제 기획팀에서 Shahzad et al.의 postmarketing requirement 분석을 참조하여 CGT regulatory strategy를 수립해야 한다. *출처: [Moreno Cell Therapy Solid Tumours Lancet Regional Health Europe 2026](https://consensus.app/papers/details/86e3ba34c4c8522281b75809d7f231fb/), [Oo FDA Gene Therapy Approvals Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Shahzad FDA CGT Assessment Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/)* *#FDA #Regulatory #Moreno #LancetRegionalHealthEurope #CellTherapy #SolidTumour #CAR-T #TIL #TCR-T #lifileucel #afamitresgene #Melanoma #SynovialSarcoma #MAGEA4 #EuropeanPerspective #Manufacturing #HospitalExemption #Allogeneic #iPSC #GeneEdited #Oo #JournalClinicalPharmacology #GeneTherapy #PlatformAligned #RiskBased #CMC #PlausibleMechanism #AdvancedManufacturing #AI #ML #DurableBenefit #EquitableAccess #Shahzad #ClinicalPharmacologyTherapeutics #CGT #Postmarketing #Requirement #Commitment #38Approvals #OrphanDesignation #ExpeditedPathway #SurrogateEndpoints #PrimaryEfficacy #SingleStudy #TransitioningDay #SolidTumor #CellTherapy #RegulatoryFragmentation #ManufacturingCost #PatientAccess #Safety #LongTermSafety #DoseSelection #DurabilityPrediction* --- ### Post 7: Spatial Transcriptomics — SCLC Spatial Ecosystem과 Pan-Cancer TME의 Transitioning 🗺️ **[Spatial Transcriptomics] SCLC 원발/림프절 전이 600K cells spatial ecosystem으로 endothelial-immune crosstalk mechanism 확인(Zhang Cell Reports Medicine 2026) + Pan-cancer 12 cancer types 56 LCPs · 13 niches로 Niche_4(poor prognosis) · Niche_11(better survival) biomarker体系재확인(Li Cell Reports Medicine 2026) + Gastric cancer lymphocyte-aggregated region(LAR) discovery로 immunotherapy biomarker 제시(Gao Nature Communications 2026): spatial transcriptomics의 Transitioning(전이) 동시 확인** Spatial transcriptomics의**다음 단계로의 전이**가 출처에서 동시에 확인된다[16][17][18]. **SCLC Spatial Ecosystem: 600K Cells + Endothelial-Immune Crosstalk Mechanism[16]** Zicheng Zhang et al.(Cell Reports Medicine 2026)의 연구는 SCLC의 spatial cellular ecosystem을 분석한다. **대상**: 75명 SCLC 환자, 105개 원발/림프절 전이 검체. **방법**: CosMx Spatial Molecular Imager. **규모**: 600,000+ cells의 comprehensive atlas. **주요 발견**: LNM-enriched malignant subclusters 3개(distinct metabolic/angiogenic programs). **Endothelial-immune crosstalk**: endothelial cells가 malignant cells 회피 + cytotoxic T cells와 functional perivascular niches 형성. **공간적 상관관계**: immune exclusion features와 상관관계. **PIHs-1(pan-immune hotspot)**: 독립적 survival 예측 인자. 핵심: SCLC 600K cells spatial atlas로 endothelial-immune crosstalk mechanism 확인 — spatial TME Transitioning. **Pan-Cancer Spatial TME: 56 LCPs · 13 Niches + Niche_4 · Niche_11 Biomarker 체계[17]** Jiarong Li et al.(Cell Reports Medicine 2026)의 연구는 pan-cancer spatial transcriptomic analysis를 수행한다. **12 cancer types · 373 samples**: 체계적 분석. **56개 Local Cellular Programs(LCPs)**: tumor spatial microenvironment(TSME) 내에서. **13개 recurrent niches**: niche-shared · niche-specific interactions. **Niche_4(macrophages + tumor cells)**: poor prognosis · immunotherapy resistance. **Niche_11(macrophages + immune cells)**: better survival · treatment response. **종양세포 + macrophages의 위치 의존성**: gene expression이 위치에 따라 결정. 핵심: Pan-cancer 12 types · 56 LCPs · 13 niches 통합 + Niche_4/Niche_11 prognostic biomarkers 체계 — spatial TME Transitioning. **Gastric Cancer LAR: Lymphocyte-Aggregated Region Immunotherapy Biomarker[18]** Sen Gao et al.(Nature Communications 2026)의 연구는 gastric cancer의 spatial regions를 분석한다. **방법**: single-cell + spatial transcriptomics 통합. **4개 공간적 영역 발견**: Lymphocyte Aggregated Region(LAR). **LAR 특성**: lymphocyte aggregates + tertiary lymphoid structures. **발견**: naive T cell abundance + T cell activation-associated pathways 상관관계. **PD1+CD27+ CD8 T cells**: CD70+LAMP3+ dendritic cells와 근접 클러스터. 핵심: Gastric cancer LAR 발견으로 immunotherapy biomarker 제시 — spatial TME Transitioning. **9월 3일 Consolidating vs 오늘의 차별점**: 9/3이 **Zhang Nature**으로 **Spatial ecotypes 9개 + cfDNA liquid biopsy**를, **Li Cell Reports Medicine**으로 **pan-cancer 56 LCPs + 13 niches**를, **Ji Cancer Chemotherapy Pharmacology**으로 **breast cancer spatial heterogeneity + BsADC targeting**을 제시했다면, 오늘은 **Zhang Cell Reports Medicine**으로 **SCLC 600K cells + endothelial-immune crosstalk mechanism**을(새 biology mechanism), **Li Cell Reports Medicine**으로 **Niche_4/Niche_11 prognostic biomarker 체계 재확인**을(biomarker 체계 Transitioning), **Gao Nature Communications**으로 **gastric cancer LAR discovery + immunotherapy biomarker**를(새 biomarker 발견) 동시에 제시한다. Consolidating(9/3) → Spatial transcriptomics Transitioning(9/4). 실무 함의: spatial transcriptomics 연구팀에서 Zhang et al.의 endothelial-immune crosstalk 분석을 참조하여 tumor microenvironment targeting strategy를 수립해야 한다. *출처: [Zhang SCLC Spatial Atlas Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b18cf8aed1117/), [Li Pan-Cancer Spatial TME Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Gao Gastric Cancer LAR Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/)* *#SpatialTranscriptomics #Zhang #CellReportsMedicine #SCLC #CosMx #600KCells #75Patients #Endothelial #ImmuneCrosstalk #PerivascularNiche #PIH1 #PanImmuneHotspot #LNM #Metabolic #Angiogenic #Li #PanCancer #12CancerTypes #56LCPs #13Niches #Niche4 #Niche11 #Macrophages #Prognosis #ImmunotherapyResistance #Survival #TreatmentResponse #Gao #NatureCommunications #GastricCancer #LAR #LymphocyteAggregatedRegion #TLS #PD1 #CD27 #CD8T #CD70 #LAMP3 #DendriticCells #TransitioningDay #SpatialAtlas #TME #Biomarker #Survival #Resistance #TargetedTherapy #Microenvironment #CellularCommunication #SpatialMapping #CancerAtlas #SolidTumor #LungCancer #GastricCancer #ImmuneEvasion #VascularImmuneCrossTalk* --- ### Post 8: 오늘의 요약 — Transitioning(전이 Day) 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 9/4(Transitioning)의 핵심: Consolidating(9/3)된 기술들이 **다음 적용 단계(Transitioning)로 이전하며 새 영역의 기술적 타당성을 입증하는 날**이었다. **1. CellxPert MCMC Steering In-Silico Perturbation + LoRA Fine-Tuning(Demir ArXiv 2026)**: Metropolis-Hasticks sampler로 masked conditional distributions 사용 — out-of-distribution artifacts 완충 + biologically interpretable trajectories 회복 — MERFISH · imaging mass cytometry 2D/3D spatial multimodal 통합. Foundation model perturbation mechanistic interpretability Transitioning. [Demir ArXiv 2026] **2. AlphaFold-Like Model Protein-Ligand Drug Discovery 데이터 격차(Singh Current Opinion Structural Biology 2026) + AlphaFold Peptide Conformational Dynamics 한계 + MD 보완 필요성(Zhang Biotechnology Advances 2026)**: AlphaFold-like model의 SBDD 적용 한계 체계적 분석 + peptide prediction conformational dynamics 한계 + ipTM score 한계 + molecular dynamics 통합 전략 제시. AI drug discovery / 단백질 AI Transitioning. [Singh Current Opinion Structural Biology 2026; Zhang Biotechnology Advances 2026] **3. Cell Therapy Solid Tumours FDA Milestone 2개(Moreno Lancet Regional Health Europe 2026) + FDA CGT 38개 Through 2025 체계적 분석(Oo Journal Clinical Pharmacology 2026) + Postmarketing Requirement 73.4% / 17.9% Primary Efficacy問題(Shahzad Clinical Pharmacology Therapeutics 2026)**: lifileucel(TIL, melanoma) + afamitresgene(TCR, synovial sarcoma) FDA 승인 + FDA platform-aligned risk-based framework 구체화 + CGT postmarketing requirement 문제 확인. FDA 규제 Transitioning. [Moreno Lancet Regional Health Europe 2026; Oo Journal Clinical Pharmacology 2026; Shahzad Clinical Pharmacology Therapeutics 2026] **4. SCLC 600K Cells Spatial Atlas(Zhang Cell Reports Medicine 2026) + Pan-Cancer 12 Types 56 LCPs 13 Niches(Li Cell Reports Medicine 2026) + Gastric Cancer LAR Discovery(Gao Nature Communications 2026)**: endothelial-immune crosstalk mechanism 확인 + Niche_4(poor prognosis) · Niche_11(better survival) prognostic biomarkers 체계 재확인 + LAR immunotherapy biomarker 제시. Spatial TME Transitioning. [Zhang Cell Reports Medicine 2026; Li Cell Reports Medicine 2026; Gao Nature Communications 2026] **5. Non-Viral Delivery Ionizable Lipid Nanoparticles 93% Protein Knockdown(Yang MedComm 2026) + 25 Dataset Benchmark Perturbation Response(Li ArXiv 2026) + PerturbNet Task 1-2 Superior + Task 3 Generalization 실패(Li ArXiv 2026)**: lipid nanoparticles single dose 후 93% knockdown 달성 + perturbation prediction benchmark로 Task 1-2에서 PerturbNet 최고 · Task 3 dataset heterogeneity 지배적. CRISPR / Foundation model Transitioning. [Yang MedComm 2026; Li ArXiv 2026] **9월 5일 전망**: Transitioning된 기술들의 다음 적용 단계 진입 속도와 기술적 타당성 입증이 핵심 변수. 특히 CellxPert의 추가 benchmark 데이터, AlphaFold-like model dataset generation 전략, FDA solid tumour cell therapy의 European regulatory framework 정착이 관전. *출처: [Demir CellxPert ArXiv 2026](https://consensus.app/papers/details/32110de5fa6651398632ec116e93577d/), [Yang Delivery Systems MedComm 2026](https://consensus.app/papers/details/87a151ebbb6759e48db9d0a028ff4597/), [Makhijani CRISPR Delivery Discover Nano 2026](https://consensus.app/papers/details/7acc7eabb0f05c51bccf0d568ec55751/), [Singh AlphaFold-like Models Current Opinion Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/), [Zhang AlphaFold Peptide Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/), [Fadini ROCKET Nature Methods 2026](https://consensus.app/papers/details/a1a4ffbc5dd75c548e55837812a33916/), [Li Systematic Comparison ArXiv 2026](https://consensus.app/papers/details/c9942dcafc665fd59f98c510a131e7e8/), [Moreno Cell Therapy Solid Tumours Lancet Regional Health Europe 2026](https://consensus.app/papers/details/86e3ba34c4c8522281b75809d7f231fb/), [Oo FDA Gene Therapy Approvals Journal Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Shahzad FDA CGT Assessment Clinical Pharmacology Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/), [Zhang SCLC Spatial Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b18cf8aed1117/), [Li Pan-Cancer TME Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Gao Gastric Cancer Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/)* *#TransitioningDay #Summary #Demir #CellxPert #MCMC #Yang #MedComm #93Knockdown #IonizableLipid #Makhijani #DiscoverNano #BaseEditing #PrimeEditing #Singh #CurrentOpinion #AlphaFoldLike #ProteinLigand #DataGap #Zhang #BiotechnologyAdvances #Peptide #ConformationalDynamics #Fadini #ROCKET #CryoEM #Li #PerturbNet #PerturbationResponse #25Datasets #Moreno #Lancet #CellTherapy #SolidTumour #TIL #TCR #lifileucel #afamitresgene #Oo #JournalClinicalPharmacology #PlatformAligned #RiskBased #Shahzad #ClinicalPharmacologyTherapeutics #Postmarketing #Requirement #38Approvals #Zhang #SCLC #600K #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Gao #GastricCancer #LAR #Transitioning #Consolidating #ClinicalTranslation #SpatialBiology #MechanisticInterpretability #PerturbationPrediction #NonViralDelivery #LipidNanoparticle #FoundationModel #Benchmark #SolidTumor #CellTherapy #FDA #Regulatory #CMC #Manufacturing #Biomarker #Prognosis #Survival #Resistance #Immunotherapy #TME #SpatialAtlas #PeptideDrugDesign #MolecularDynamics #VirtualScreening #ConfidenceMetric #EpigenomeEditing* --- ## Sources [1] [Demir et al. — CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation. ArXiv 2026](https://consensus.app/papers/details/32110de5fa6651398632ec116e93577d/) [2] [Yang et al. — Delivery Systems for Therapeutic Genome Editing: Challenges, Innovations, and Future Perspectives. MedComm 2026](https://consensus.app/papers/details/87a151ebbb6759e48db9d0a028ff4597/) [3] [Makhijani et al. — From mechanism to medicine: CRISPR‒Cas9 delivery strategies, therapeutic applications and translation challenges. Discover Nano 2026](https://consensus.app/papers/details/7acc7eabb0f05c51bccf0d568ec55751/) [4] [Singh et al. — More Protein-Ligand data is needed for AlphaFold-like Models to enable drug discovery. Current Opinion in Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/) [5] [Zhang et al. — AlphaFold-based peptide structure prediction: Opportunities, limitations, and future directions. Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/) [6] [Fadini et al. — AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network. Nature Methods 2026](https://consensus.app/papers/details/a1a4ffbc5dd75c548e55837812a33916/) [7] [Li et al. — A Systematic Comparison of Single-Cell Perturbation Response Prediction Models. ArXiv 2026](https://consensus.app/papers/details/c9942dcafc665fd59f98c510a131e7e8/) [8] [Moreno et al. — Advances in cell therapy for solid tumours: European perspective and future directions. Lancet Regional Health Europe 2026](https://consensus.app/papers/details/86e3ba34c4c8522281b75809d7f231fb/) [9] [Oo et al. — FDA Gene Therapy Approvals (1998–2025): Current Status, Regulatory Evolution, and Future Directions. Journal of Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/) [10] [Shahzad et al. — Assessment of United States Food and Drug Administration Approval of Cell and Gene Therapies. Clinical Pharmacology & Therapeutics 2026](https://consensus.app/papers/details/24705d5037e85bcb9d9863f1aabca040/) [11] [Zhang et al. — Single-cell spatial transcriptomics reveals tumor microenvironment heterogeneity in primary and lymph node-metastatic small cell lung cancer. Cell Reports Medicine 2026](https://consensus.app/papers/details/dc8b02ddbfcb574fb92b18cf8aed1117/) [12] [Li et al. — Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment. Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/) [13] [Gao et al. — A spatially resolved atlas of gastric cancer characterises a lymphocyte-aggregated region. Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/)
Research Pulse·2026-09-04
Research Pulse — 2026-09-04
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
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