1 Repaso: scRNA-seq vs bulk-RNA-seq

Bulk RNA-seq: mide expresión promedio en un tejido; útil para detectar cambios globales, pero oculta heterogeneidad celular.
- Insuficiente para: estudios de desarrollo temprano o tejidos complejos como el cerebro.

Imagen tomada de: Yan et al. 2024. Int J Biol Sci

1.1 Single cell RNA-seq (scRNA-seq)

Primera publicación en 2009 (Tang et al, 2009)
Popularidad en 2014
Estimar la distribución de los niveles de expresión de cada gen en una población celular.
Análisis a nivel de célula individual
Permite revelar tipos celulares (poblaciones raras)
Analizar las dinámicas temporales y heterogeneidad.

Imagen tomada de: Single cell RNA sequencing

1.2 Heterogeneidad: Diversidad interna de un sistema o de un conjunto

🔬 No todas las células son iguales: incluso dentro de un mismo tejido, existen diferencias en tipos celulares, estados funcionales y niveles de expresión génica.
🧬 Variabilidad genética y transcriptómica: cada célula puede expresar distintos genes o cantidades de RNA, reflejando funciones específicas.
🩺 Relevancia en enfermedad: la heterogeneidad celular explica por qué un tumor, por ejemplo, contiene subpoblaciones con distintos comportamientos (algunas más agresivas, otras más sensibles a tratamiento).

Visualización a través de UMAP (Uniform Manifold Approximation and Projection):

Imagen tomada de: Explicación completa de un UMAP

1.3 Preguntas que se pueden responde scRNA-seq

Descubrir tipos celulares nuevos o raros.
Identificar composición celular diferencial entre sano vs. enfermo.
Comprender la diferenciación celular en desarrollo y regeneración.
Analizar plasticidad y dinámicas de expresión en células individuales.
Construir atlas celulares/genéticos → catálogo completo de diversidad celular.
Aplicaciones en investigación básica y medicina personalizada.

Image source: Kageyama, et al. 2018. Front. Neurosci

1.4 General Pipeline for scRNA‑seq with Seurat

Step	Purpose	Seurat Command
Preprocessing	Load raw count matrix and create Seurat object	`Read10X()`, `CreateSeuratObject()`
Quality Control (QC)	Filter cells/genes based on metrics (e.g., nFeature_RNA, percent.mt)	`subset()`, `PercentageFeatureSet()`
Normalization	Adjust counts for sequencing depth	`NormalizeData()` or `SCTransform()`
Feature Selection	Identify variable genes	`FindVariableFeatures()`
Dimensionality Reduction	Reduce dimensions for visualization	`ScaleData()`, `RunPCA()`, `RunUMAP()`, `RunTSNE()`
Clustering & Annotation	Group cells and assign biological meaning	`FindNeighbors()`, `FindClusters()`, `FindAllMarkers()`
Dataset Integration	Combine multiple datasets, remove batch effects	`FindIntegrationAnchors()`, `IntegrateData()`
Downstream Analysis	Specific goals: DEGs, trajectories, cell communication	`FindMarkers()`, `CellCycleScoring()`, external packages

1.5 Variations of scRNA‑seq

Type of Analysis	Contribution
Unimodal (RNA only)	Cellular heterogeneity in gene expression
Multimodal (RNA + others, e.g., CITE‑seq)	Regulation, proteomics, and biological context
Trajectories	Temporal dynamics of biological processes
Cellular Interactions	Communication and functional networks
Dataset Integration	Comparison across conditions

1.6 What is CITE-Seq?

CITE‑seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) is a multimodal single‑cell technique that combines RNA sequencing with protein profiling at the level of individual cells.

1.6.1 📌 Key points about CITE‑seq

Simultaneous measurements → captures both the transcriptome (scRNA‑seq) and surface proteins using antibody‑derived tags (ADTs).
Highly multiplexed → allows detection of many protein markers in parallel, alongside unbiased RNA profiling.
Single‑cell resolution → thousands of cells can be analyzed at once, integrating RNA and protein data.
Multimodal context → CITE‑seq is part of the broader multimodal analysis frontier in single‑cell genomics, where multiple data types (RNA, ATAC, methylation, perturbations) are measured in the same cell.
Applications → improves cell type identification, refines clustering, and reveals functional states that RNA alone might miss.

Schematic figure of (A) the CITE-seq antibody linked with the barcoded oligo (B) the CITE-seq protocol. Credit: Winston & Gregory Timp (2020), license: CC-BY 4.0

1.7 Overview

Schematic of the dry lab workflow for CITE-Seq

1.8 General Pipeline for CITE‑seq

Step	Purpose	Seurat Command
Preprocessing	Load RNA counts and antibody‑derived tags (ADT)	`Read10X()`, `CreateSeuratObject()`
Quality Control (QC)	Filter cells/genes based on RNA metrics and ADT counts	`PercentageFeatureSet()`, `subset()`
Normalization (RNA)	Adjust RNA counts for sequencing depth	`SCTransform()` or `NormalizeData()`
Normalization (ADT)	Center and scale protein counts	`NormalizeData(assay = "ADT")`, `ScaleData(assay = "ADT")`
Feature Selection (RNA)	Identify variable genes	`FindVariableFeatures()`
Dimensionality Reduction	Reduce dimensions for RNA and ADT separately	`RunPCA(assay = "RNA")`, `RunPCA(assay = "ADT")`
Multimodal Integration	Combine RNA + ADT signals	`FindMultiModalNeighbors()`, `RunUMAP(nn.name = "weighted.nn")`
Clustering & Annotation	Group cells and assign biological meaning	`FindClusters()`, `FindAllMarkers()`
Downstream Analysis	Discover enriched proteins per cluster, refine cell types	`FindMarkers(assay = "ADT")`, external multimodal workflows

1.9 scRNA-seq vs scRNA-seq + CITE‑seq

Approach	Type	Purpose
scRNA‑seq	Unimodal (RNA only)	Characterize transcriptomic heterogeneity: identify cell types, states, and gene expression dynamics based solely on RNA.
CITE‑seq	Multimodal (RNA + proteins)	Integrate RNA + surface proteins: refine cell type annotation, validate clusters, and capture functional context beyond transcriptomics.