added LNP figures and notebook

Author: jsdearbo

.github/workflows/deploy.yml

@@ -57,6 +57,13 @@ jobs:
           cp assets/images/headshot.jpg ai_studio/public/assets/images/headshot.jpg
           cp Dearborn_CV_March_2026.pdf ai_studio/public/Dearborn_CV_March_2026.pdf
 
+      - name: Stage LNP analysis assets
+        run: |
+          mkdir -p ai_studio/public/assets/images/lnp
+          cp assets/images/lnp/*.png ai_studio/public/assets/images/lnp/
+          mkdir -p ai_studio/public/notebooks
+          cp assets/notebooks/lnp_10x.html ai_studio/public/notebooks/lnp_10x.html
+
       - name: Build
         working-directory: ai_studio
         run: npm run build

ai_studio/App.tsx

@@ -3,7 +3,7 @@ import React, { useState } from 'react';
 import Navigation from './components/Navigation';
 import { COMP_BIO_PROJECTS, ML_PROJECTS, PUBLICATIONS, RESEARCH_AREAS, PORTFOLIO_CASES, PRESENTATIONS, ML_EVIDENCE_SECTIONS } from './constants';
 import { PortfolioCase } from './types';
-import { Github, Linkedin, Mail, FileText, ExternalLink, Code2, Beaker, Cpu, Database, ArrowRight, BarChart2, FlaskConical } from 'lucide-react';
+import { Github, Linkedin, Mail, FileText, ExternalLink, Code2, Beaker, Cpu, Database, ArrowRight, BarChart2, FlaskConical, FileCode } from 'lucide-react';
 
 const accentBorderMap: Record<string, string> = {
   blue: 'border-l-blue-600',
@@ -215,7 +215,18 @@ const App: React.FC = () => {
           <div key={i} className="glass p-8 rounded-3xl border-l-4 border-l-purple-600 space-y-6">
             <h3 className="text-2xl font-bold">{c.title}</h3>
 
-            <FigurePlaceholderBlock label={c.figurePlaceholderLabel} />
+            <FigurePlaceholderBlock label={c.figurePlaceholderLabel} imagePath={c.figureImagePath} />
+
+            {c.notebookPath && (
+              <a
+                href={c.notebookPath}
+                target="_blank"
+                rel="noreferrer"
+                className="inline-flex items-center gap-2 text-sm text-blue-400 hover:text-blue-300 transition-colors"
+              >
+                <FileCode size={14} /> View Analysis Notebook <ExternalLink size={12} />
+              </a>
+            )}
 
             <div className="grid grid-cols-1 md:grid-cols-2 gap-6">
               <div>
@@ -389,8 +400,50 @@ const App: React.FC = () => {
         </div>
       </div>
 
-      {/* Shiny demo — secondary link */}
-      <div className="glass p-6 rounded-2xl border border-white/10 flex flex-col md:flex-row items-start md:items-center justify-between gap-4">
+      {/* Published analyses — secondary links */}
+      <div className="space-y-4">
+        {/* ACS Nano scRNA-seq */}
+        <div className="glass p-6 rounded-2xl border border-white/10 flex flex-col md:flex-row items-start md:items-center justify-between gap-4">
+          <div className="space-y-2">
+            <div className="flex items-center gap-2 flex-wrap">
+              <span className="text-[10px] font-bold px-2 py-0.5 rounded uppercase tracking-wider bg-green-500/20 text-green-400">
+                Published
+              </span>
+              <span className="text-[10px] font-bold px-2 py-0.5 rounded uppercase tracking-wider bg-white/10 text-gray-400">
+                R · Seurat · scRNA-seq · 10x
+              </span>
+            </div>
+            <h4 className="font-semibold text-base">
+              Single-Cell Transcriptomics — Intramuscular LNP Immune Response
+            </h4>
+            <p className="text-sm text-gray-500 max-w-xl">
+              Identified 9 immune populations in injected muscle. Myeloid infiltrates drive ISG and
+              inflammasome programs in response to SM-102 eLNP. Figure 3,{' '}
+              <span className="italic">ACS Nano</span> (2024).
+            </p>
+          </div>
+          <div className="flex items-center gap-3 shrink-0">
+            <a
+              href="/notebooks/lnp_10x.html"
+              target="_blank"
+              rel="noreferrer"
+              className="flex items-center gap-2 glass px-4 py-2 rounded-lg text-sm font-medium text-blue-400 hover:text-blue-300 hover:border-blue-500/50 transition-all"
+            >
+              <FileCode size={14} /> View Notebook
+            </a>
+            <a
+              href="https://doi.org/10.1021/acsnano.4c08490"
+              target="_blank"
+              rel="noreferrer"
+              className="flex items-center gap-2 glass px-4 py-2 rounded-lg text-sm font-medium text-gray-400 hover:text-gray-300 hover:border-white/20 transition-all"
+            >
+              View Paper <ExternalLink size={14} />
+            </a>
+          </div>
+        </div>
+
+        {/* Shiny demo — secondary link */}
+        <div className="glass p-6 rounded-2xl border border-white/10 flex flex-col md:flex-row items-start md:items-center justify-between gap-4">
         <div className="space-y-2">
           <div className="flex items-center gap-2 flex-wrap">
             <span className="text-[10px] font-bold px-2 py-0.5 rounded uppercase tracking-wider bg-green-500/20 text-green-400">
@@ -417,6 +470,7 @@ const App: React.FC = () => {
         >
           Open app <ExternalLink size={14} />
         </a>
+        </div>
       </div>
     </section>
   );

ai_studio/constants.tsx

@@ -151,6 +151,27 @@ export const PORTFOLIO_CASES: PortfolioCase[] = [
     signalsForML: "Pathway-level summarization of high-dimensional transcriptomic data, statistical pipeline design, DESeq2 normalization and modeling.",
     signalsForBio: "Bulk RNA-seq end-to-end, differential expression, GSEA, adipose and cancer biology, published collaborative analysis.",
     figurePlaceholderLabel: "Figure: Volcano plot — gWAT DEGs at 3 and 6 weeks"
+  },
+  {
+    title: "Single-Cell Transcriptomics of the Intramuscular mRNA/LNP Immune Response",
+    problem: "mRNA/LNP vaccines drive adjuvancy through ionizable lipids, but which immune cell types in injected muscle are activated and what gene programs do they execute? Answering this at single-cell resolution required building a complete scRNA-seq pipeline from raw count matrices.",
+    data: "10x Chromium scRNA-seq of CD45+ immune cells from SM-102 eLNP- and PBS-injected mouse muscle (n=4/group, pooled into 2 samples; 5,511 LNP-treated + 1,189 PBS barcodes after QC). Hashtag demultiplexing via TotalSeq-A.",
+    whatIBuilt: "Full Seurat pipeline: multi-sample integration, SCTransform normalization with MT% regression, UMAP clustering, automated cell type annotation (scType) cross-validated against manual marker-based annotation, and differential expression across 9 immune populations.",
+    methodsStack: [
+      "R / Seurat — multi-sample integration, SCTransform, FindNeighbors, FindClusters",
+      "UMAP dimensionality reduction (30 PCs)",
+      "scType automated cell type scoring (immune system reference)",
+      "Manual annotation using canonical marker panels (Cx3cr1/Ccr2, S100a8/S100a9, Flt3/Xcr1)",
+      "FindAllMarkers — per-cluster differential expression across 9 cell types",
+      "Violin and split plots of ISGs, inflammasome, and cytokine genes across PBS vs. LNP conditions"
+    ],
+    result: "Identified 9 immune populations in injected muscle. Neutrophils and monocytes from SM-102-injected mice upregulate ISGs (Irf7, Isg15), inflammasome components (Nlrp3, Il1b), and cytokines (Tnf, Cxcl2) — published as Figure 3, Dowell et al., ACS Nano 2024 (doi:10.1021/acsnano.4c08490).",
+    whyItMatters: "Single-cell resolution revealed that myeloid infiltrates — not muscle tissue in bulk — are the source of the LNP-driven inflammatory program. This reframes how vaccine lipid adjuvancy works and provides a design principle distinguishing vaccine-grade (Class A) from therapeutic-grade (Class B) ionizable lipids.",
+    signalsForML: "Unsupervised clustering of high-dimensional count data, evaluation of automated cell type classification (scType) against manual ground truth, per-cluster differential ranking from FindAllMarkers — analogous to unsupervised representation learning with post-hoc class label assignment and feature attribution.",
+    signalsForBio: "Multi-sample hashtag demultiplexing, QC filtering (nFeature > 500, MT% < 10%), SCTransform normalization with confounder regression, marker-based cell type validation across monocytes, neutrophils, DCs, NK cells, B cells, T cells, pDCs, and RBCs.",
+    figurePlaceholderLabel: "UMAP — CD45+ immune infiltrates, PBS vs. SM-102 eLNP (manual annotation)",
+    figureImagePath: "/assets/images/lnp/umap_split.png",
+    notebookPath: "/notebooks/lnp_10x.html",
   }
 ];
 

ai_studio/types.ts

@@ -48,4 +48,6 @@ export interface PortfolioCase {
   signalsForML: string;
   signalsForBio: string;
   figurePlaceholderLabel: string;
+  figureImagePath?: string;
+  notebookPath?: string;
 }