Comparative Transcriptomics of Adherent and Suspension Chicken Fibroblast Cell Lines for the Optimization of Cultivated Meat Processes

This page showcases a real-world application of the tucca-rna-seq workflow in a study by members of the Tufts University Center for Cellular Agriculture (TUCCA).

The Research Paper

Authors: Elizabeth J. Contreras, Archana Nagarajan, Benjamin H. Bromberg, Mason P. Villegas, David L. Kaplan

Published in: Food Research International (2026).

Data: raw RNA-seq reads are deposited in NCBI BioProject PRJNA1331774.

This study examines the transcriptomic changes that occur when chicken cells are adapted for large-scale cultivated meat production.

Read in Food Research InternationalbioRxiv preprintRaw data (NCBI)

The Scientific Challenge: Adapting Cells for Cultivated Meat

For cultivated meat to become a reality, we need to grow large quantities of cells efficiently. Many cells used in research naturally grow attached to a surface (i.e. adherent), and that anchorage is the problem at scale: an adherent culture can only hold as many cells as its surface area allows, so growing more of them means more flasks or more microcarriers, which adds cost and labour with every step up. Cells grown floating in a nutrient-rich medium (i.e. in suspension) fill the entire volume of a stirred-tank bioreactor instead, the same kind of vessel that industrial fermentation already operates at large scale. Production then scales with tank volume rather than surface area, and that volumetric scale-up is what makes the cost targets for cultivated meat realistic. This is why industrial-scale production favours suspension growth.

The study used the chicken (Gallus gallus) embryonic fibroblast line DF-1, comparing adherent DF-1 against a suspension-adapted derivative of the same line. The transition from adherent to suspension growth is a complex biological process. To engineer this trait effectively, researchers needed to understand the precise transcriptomic and molecular changes that allow cells to survive and grow without attachment.

The tucca-rna-seq Solution: An Engine for Discovery

The researchers used time-series bulk RNA sequencing to capture the transcriptional dynamics at five key stages of the adaptation process. For this study, an early development version of tucca-rna-seq was used, and that work helped shape the stable, public release of the workflow available today.

The workflow served as the analytical engine that turned the massive raw dataset (n = 4 for each time point) into biological insights by automating the entire pipeline:

• Core Processing & QC: Handled initial quality control (FastQC), genome alignment (STAR), and accurate transcript quantification (Salmon), generating reports with MultiQC and Qualimap.
• Differential Gene Expression: The workflow’s DESeq2 module identified which genes significantly changed their expression levels over the adaptation timeline.
• Functional Enrichment: Using clusterProfiler, the workflow performed Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to reveal which biological pathways were activated or suppressed.
• Foundation for Custom Analysis: The workflow produced normalized, quality-controlled expression data that served as the direct input for the paper’s custom downstream analyses, including the time-series soft clustering with Mfuzz.

Key Scientific Findings Uncovered by the Workflow

By leveraging the multi-faceted analysis from tucca-rna-seq, the researchers uncovered a detailed timeline of cellular adaptation, revealing three major findings.

1. An Acute Stress Response at the Point of Transition

Gene Set Enrichment Analysis (GSEA) of the early timepoints (T2 and T3) revealed a dramatic, but transient, stress response. As cells lost their attachment, they activated pathways related to survival and crisis management, including autophagy and MAPK signaling, while simultaneously shutting down resource-intensive processes like DNA replication. This demonstrated that cells initially enter a survival mode to cope with the new, challenging environment.

2. A Permanent Reprogramming of Cellular Machinery

Differential Gene Expression (DGE) analysis across all timepoints identified a core set of genes that were permanently rewired in the suspension-adapted cells. This long-term strategy involved:

• Metabolic Suppression: Pathways like steroid biosynthesis and ribosome biogenesis were consistently downregulated. This finding, uncovered too by GSEA, helps explain the observed smaller size and slower growth rate of suspension cells, indicating they adopt a more energy-efficient state.

• Dynamic Regulation of Cell Adhesion: The analysis revealed a fascinating two-phase response. Initially, cells upregulated focal adhesion and ECM-receptor interaction pathways in a desperate attempt to re-establish attachment. However, in fully adapted cells, these same pathways were sustainably downregulated below their original baseline, marking a true commitment to an anchorage-independent phenotype.

• Enhanced Signaling: In contrast to the downregulation of other systems, pathways related to cytokine-cytokine receptor interaction were consistently activated, suggesting that as cells lose physical contact with a substrate, they increase their reliance on chemical signaling to coordinate behavior.

3. A Proposed Molecular Mechanism for Suspension Adaptation

By integrating the results from DGEA and pathway analysis, the researchers formulated a novel hypothesis for how these cells achieve suspension proficiency. The data suggests a mechanism where:

1. Oxidative stress activates the MAPK p38δ pathway.
2. This leads to the sequestration of TEAD transcription factors in the cytoplasm.
3. The protein YAP, now unable to bind with TEAD, instead interacts with FoxO1.
4. This new YAP-FoxO1 complex simultaneously triggers the production of protective antioxidants and downregulates the adhesion genes typically activated by YAP-TEAD.

This compelling hypothesis, derived directly from the workflow’s outputs, provides a concrete set of targets for future research and bioengineering efforts.

Conclusion

This study shows how tucca-rna-seq serves as more than a data-processing tool: it helps generate hypotheses. By providing a reproducible analysis from raw reads through functional enrichment, the workflow let researchers move from raw sequence reads to a novel biological mechanism in cellular agriculture.

Reference

Contreras, E. J., Nagarajan, A., Bromberg, B. H., Villegas, M. P., & Kaplan, D. L. (2026). Comparative transcriptomics of adherent and suspension chicken fibroblast cell lines for the optimization of cultivated meat processes. Food Research International. https://doi.org/10.1016/j.foodres.2026.118368

Data: Raw RNA-seq reads are deposited in NCBI BioProject PRJNA1331774.