Dogma-Seq: Innovative Multimodal Chromatin and RNA Profiling

Advancements in sequencing technologies continue to refine our understanding of gene regulation, yet integrating multiple layers of cellular information remains a challenge. Dogma-Seq is an emerging approach that enables the simultaneous profiling of chromatin accessibility, RNA expression, and protein levels within individual cells, offering a more comprehensive view of cellular states.

By capturing these molecular modalities together, researchers gain deeper insights into how gene expression is regulated across biological processes and disease contexts.

Core Principles of Dogma-Seq

Dogma-Seq is built on the premise that cellular function is dictated by the interplay between chromatin structure, transcriptional activity, and protein expression. Traditional sequencing methods examine these molecular layers separately, limiting the ability to capture dynamic regulatory interactions. By integrating chromatin accessibility, RNA expression, and protein abundance within the same single-cell framework, Dogma-Seq provides a unified perspective on gene regulation. This approach allows researchers to analyze how chromatin remodeling influences transcription and how these transcriptional changes translate into protein-level effects.

A key advantage of Dogma-Seq is its single-cell resolution, which avoids the averaging effects seen in bulk sequencing. Cellular heterogeneity plays a significant role in development, disease progression, and therapeutic response, yet conventional methods obscure rare subpopulations by blending signals from diverse cell types. By preserving the molecular identity of individual cells, Dogma-Seq reveals distinct regulatory programs that would otherwise be masked. This is particularly valuable in cancer research, where tumor heterogeneity drives differential drug responses, and in neurobiology, where diverse neuronal subtypes have unique transcriptional and epigenetic landscapes.

The method relies on transposase-based chromatin profiling, barcoded RNA sequencing, and antibody-based protein detection, all within the same single-cell workflow. Precise molecular barcoding ensures each data type is accurately linked back to its originating cell. Advances in microfluidics and droplet-based partitioning have facilitated high-throughput profiling while maintaining data integrity.

Multimodal Analysis of Chromatin Accessibility

Chromatin accessibility determines whether transcriptional machinery can engage with DNA, making it central to gene regulation. Dogma-Seq enhances this analysis by integrating chromatin accessibility profiling with transcriptomic and proteomic data. Traditional methods like ATAC-seq or DNase-seq provide insights into open chromatin regions but lack the ability to directly correlate these findings with gene expression and protein output at the single-cell level. By capturing chromatin accessibility within a multimodal framework, Dogma-Seq tracks how structural changes influence transcription and translation in ways isolated assays cannot.

By linking chromatin accessibility and transcriptional activity within the same cell, researchers can associate enhancers and promoters with active or repressed transcriptional programs. This approach has been particularly useful in lineage commitment studies, where chromatin configurations precede transcriptional shifts that drive cell fate decisions. Dogma-Seq has revealed how chromatin remodeling primes certain genes for activation while silencing others, uncovering regulatory hierarchies that were previously difficult to resolve.

The method also identifies cell-to-cell variability in chromatin accessibility, which is especially relevant in heterogeneous tissues. In cancer, single-cell chromatin profiling has uncovered subpopulations with distinct regulatory landscapes that influence tumor progression and therapy resistance. In developmental biology, it has shed light on transient chromatin states guiding differentiation. By preserving the molecular context of chromatin accessibility within individual cells, Dogma-Seq clarifies how chromatin dynamics shape cellular identity and function.

Combined RNA and Protein Profiling

Integrating RNA and protein profiling within Dogma-Seq provides a clearer understanding of how gene expression translates into functional cellular states. While transcriptomic analysis reveals which genes are being transcribed, protein abundance ultimately dictates cellular behavior. Traditional sequencing assumes a direct correlation between mRNA levels and protein expression, but post-transcriptional regulation—such as mRNA stability, translation efficiency, and protein degradation—can alter protein abundance independently of RNA levels. By capturing both molecular layers within the same single-cell framework, Dogma-Seq identifies cases where transcriptional activity does not predict protein output, revealing regulatory mechanisms that would otherwise remain obscured.

Single-cell protein measurements are particularly valuable in understanding dynamic cellular responses. Transcriptional changes may precede protein-level shifts by hours or days, meaning RNA analysis alone may not fully capture molecular transitions. By simultaneously measuring RNA and protein, Dogma-Seq reveals delays between transcription and translation, as well as instances where proteins are synthesized in response to environmental cues despite low mRNA levels. This has been instrumental in studying stress responses, where rapid protein-level adaptations occur independently of transcriptional changes, and metabolic regulation, where enzyme activity is primarily governed by post-translational modifications rather than transcriptional upregulation.

Beyond individual gene expression patterns, Dogma-Seq facilitates the study of protein-protein interactions and co-expression networks at the single-cell level. Many cellular processes, including signal transduction and metabolic pathways, rely on coordinated protein interactions, yet traditional sequencing methods fail to capture these relationships. By integrating RNA and protein profiling, researchers can analyze how transcriptional programs give rise to specific protein complexes and determine how these interactions vary across cellular states. This has proven useful in studying diseases where protein dysfunction plays a central role, such as neurodegenerative disorders, where misfolded proteins accumulate despite normal transcriptional profiles, or cancer, where aberrant protein signaling drives tumor progression despite seemingly unremarkable gene expression patterns.

Key Steps in Sequencing Workflow

The Dogma-Seq workflow preserves chromatin, RNA, and protein integrity while ensuring each molecular component is accurately linked to its originating cell. The process begins with single-cell isolation, typically achieved through microfluidic droplet partitioning or fluorescence-activated cell sorting (FACS). This step is crucial for maintaining cellular identity, as each cell must be processed individually to prevent signal mixing. Once isolated, cells are lysed under carefully optimized conditions that preserve chromatin structure while releasing RNA and proteins without excessive degradation.

Following lysis, chromatin accessibility is profiled using a transposase-based method, such as ATAC-seq, which selectively inserts sequencing adapters into open chromatin regions. Simultaneously, RNA molecules are captured and barcoded using oligonucleotide-labeled primers, allowing for single-cell transcript identification. Protein profiling is achieved through oligonucleotide-conjugated antibodies that bind to specific surface or intracellular markers, enabling quantitative protein detection through sequencing. The integration of these three modalities requires precise barcoding strategies to ensure that chromatin, RNA, and protein data remain correctly linked to their respective cells.

Interpretation of Combined Data Sets

Integrating chromatin accessibility, RNA expression, and protein abundance into a unified analytical framework presents both opportunities and challenges. Simultaneously assessing these molecular layers allows researchers to construct more accurate models of gene regulation, but the complexity of the data requires sophisticated computational approaches. Standard bioinformatics pipelines designed for single-modality sequencing are often insufficient for handling multimodal datasets, necessitating specialized algorithms capable of linking chromatin states, transcriptional activity, and protein expression patterns.

A critical aspect of this integration is aligning sequencing reads across different molecular modalities while accounting for technical variability introduced during library preparation and sequencing. Errors in barcode assignment or batch effects can obscure true biological signals, making rigorous quality control and normalization essential for reliable interpretation.

Once data has been processed, analytical strategies focus on identifying coordinated regulatory mechanisms that span chromatin, RNA, and protein levels. By correlating chromatin accessibility with gene expression, researchers can pinpoint regulatory elements driving transcriptional activation or repression. Protein abundance data distinguishes between transcriptionally active genes that produce functional protein products and those subject to translational repression or rapid degradation.

This type of analysis has been particularly informative in studying cellular differentiation, where chromatin remodeling primes genes for activation before transcriptional changes become detectable, and translational control fine-tunes protein expression. By leveraging machine learning and network-based modeling, researchers can extract meaningful patterns from complex datasets, uncovering regulatory hierarchies that govern cell fate decisions, disease progression, and therapeutic responses.