What RNA Sequencing & Transcriptomics got wrong, from 39 theses

Tried and failed

Single-cell RNA sequencing unsupervised clustering applied to Single-cell gene expression profiles. Reason: Misannotated genes artifactually drove false cluster segregation

Investigation of maturation and survival of human long-lived plasma cells using integrated single-cell analysis · Georgia Tech · PhD

Tried and failed

Uniform quality filtering thresholds across heterogeneous samples applied to single-cell RNA sequencing data. Reason: Discarded valid cell subpopulations with naturally lower transcriptional output and baseline counts

Screening Potential Treatments of Hepatocellular Carcinoma through Single-Cell RNA Sequencing and the Connectivity Map · Harvard · Master's

Considered and rejected

Considered and rejected: Rejected dimensionality reduction (UMAP) on whole/new transcriptomes using all detected genes because it segregated cells by sampling timepoint rather than differentiation state due to cell cycle/metabolism confounding; restricted to T cell differentiation TF module.

Molecular regulation of CD8+ T cell stemness and function · University of Washington · PhD

Tried and failed

reference mapping algorithms for cell type annotation applied to single-cell transcriptomic data integration. Outcome: did not generalise. Reason: insufficient resolution without artifactual batch-integration forcing

In vitro endoderm emergence and self-organisation in the absence of extraembryonic tissues and embryonic architecture · EPFL · PhD

Tried and failed

single-cell RNA sequencing applied to memory T cell subpopulations. Outcome: no signal. Reason: technical batch effects and severe underrepresentation of one target subpopulation confounded clustering and differential expression

Investigating the heterogeneity of the human immune memory compartment · Imperial · PhD

Considered and rejected

Considered and rejected: Single-cell transcriptomics without feature barcoding, rejected due to technical noise and severe batch effects across multiple knockout populations

Functional impact of inactivating mutations in epigenetic regulators in cancer · Imperial · PhD

Considered and rejected

Considered and rejected: Single-cell transcriptomic analyses performed per individual sample independently were rejected because inter-sample biological variation prevented drawing generalisable cross-sample conclusions.

Molecular characterisation of ASXL1-mutant clonal haematopoiesis · Oxford · PhD

Tried and failed

lentiviral barcode capture via single-cell RNA sequencing applied to differentiated human pluripotent stem cells. Outcome: no signal. Reason: EF-1alpha promoter driven barcoded transcripts were not captured in scRNA-seq despite positive qPCR detection

Developing induced pluripotent stem cell derived cardiomyocytes as a model to study Friedreich's ataxia · Imperial · PhD

Lost to a baseline

Single-cell RNA sequencing captured ~10% of mRNA, whereas targeted ExSeq yielded ~62% relative to smFISH/ExFISH.

Spatially precise in situ transcriptomics in intact biological systems · MIT · PhD

Lost to a baseline

Slide-seq (version 1) had approximately 2.7% the capture efficiency of Drop-seq single-cell RNA sequencing.

Technologies for assaying the spatial position of biomolecules in situ · Harvard · PhD

Tried and failed

nuclear run-on nascent RNA sequencing using HPDP-biotin applied to low-input mammalian cell samples. Outcome: no signal. Reason: insufficient starting cell number resulted in undetectable RNA yield and poor library quality

NAB2-STAT6 DRIVES AN EGR1-DEPENDENT NEUROENDOCRINE PROGRAM IN SOLITARY FIBROUS TUMORS · Penn · PhD

Considered and rejected

Considered and rejected: Rejected using spatial transcriptomics in place of LCM-RNA-seq due to lower depth of transcript coverage.

Volatile hypoxia signatures in oesophageal adenocarcinoma · Imperial · Doctorate

Tried and failed

hybrid-capture RNA sequencing de novo assembly applied to low-biomass intracellular parasite transcriptome. Outcome: data insufficient. Reason: hybrid capture yielded insufficient read coverage to reconstruct target transcripts de novo

Analysis of transcription of the Plasmodium interspersed repeat (pir) multigene family across the life cycle of Plasmodium species · Imperial · PhD

Tried and failed

differential expression analysis on single-cell RNA sequencing applied to whole tissue disease profiling. Outcome: no signal. Reason: gene dropout effects in single-cell sequencing obscured differences across the whole tissue level

RIPK1-Regulated Neuroinflammation and Cell Death in Amyotrophic Lateral Sclerosis and Alzheimer’s Disease · Harvard · PhD

Tried and failed

pseudobulk differential expression analysis applied to single-cell RNA sequencing data. Outcome: data insufficient. Reason: low recovered cell counts per cell type across participant groups

Single-Cell Biology of Respiratory Viral Infections in the Nasal Mucosa · Harvard · PhD

Tried and failed

differential expression with small sample sizes applied to single-cell RNA sequencing data. Outcome: data insufficient. Reason: low biological replicate counts cause severe statistical underpowering and very high false discovery rates

A novel approach to power analysis for differential expression in scRNA-seq data · Imperial · Master's

Tried and failed

differential expression testing without donor random effects applied to single-cell RNA sequencing data. Reason: omitting donor/batch random effects led to massive inflation of false positive rates (type I error)

Cell states and neuronal vulnerabilities in neurodegenerative diseases · Harvard · PhD

Tried and failed

pseudobulk differential expression analysis applied to single-nucleus RNA sequencing in animal disease models. Outcome: no signal. Reason: high within-condition biological variance coupled with limited biological replicates underpowered statistical detection

Single-cell RNA-sequencing in epilepsy; discovery of cell-types, pathways, and drug targets · Imperial · PhD

Tried and failed

differential expression analysis on low-count conditions applied to bacterial transcriptomes across environmental treatments. Outcome: data insufficient. Reason: statistical robustness was insufficient due to low target gene read counts in experimental condition

Systematic Investigation into EndoRiftia Flagellar System Suggests Possibility of System Co-Option for Additional Functionality · Harvard · Master's

Tried and failed

reference-based transcriptomic deconvolution algorithms applied to spatial transcriptomics cell abundance estimation. Outcome: did not generalise. Reason: algorithms yielded inaccurate and inconsistent abundance estimates when benchmarked against ground-truth spatial proteomics single-cell data

Deciphering Virus-Mediated Remodeling of the Lymphoma Tumor Microenvironment Through Spatial Multi-Omics · Harvard · PhD

Tried and failed

spatial differential expression baseline methods applied to spatially resolved transcriptomics data. Reason: failed to disentangle cell phenotype proportions from within-phenotype spatial gene expression variation

Generative modeling of the tumor microenvironment : deconvolution, completion, and integration · UT Austin · PhD

Tried and failed

PCA on graph high-pass filtered spatial signals applied to spatial transcriptomics data. Outcome: no signal. Reason: tissue-wide spatial averaging yielded noisy, non-localized patterns across the whole tissue

Fundamental representations of regions and interactions in spatial transcriptomics · MIT · PhD

Tried and failed

dissociated single-cell RNA sequencing applied to spatially localized regional gene expression differences. Outcome: no signal. Reason: loss of spatial context during tissue dissociation obscured sharp anatomical boundary marker expression

SPATIOTEMPORAL PROFILING OF GENE EXPRESSION IN HEALTH AND DISEASE · Cornell · PhD

Tried and failed

multimodal clustering with image and transcriptomics data applied to tissue spatial domain identification. Reason: optical blurriness artifacts in images misled clustering away from true biological domains

Machine learning methods for the analysis of multi-modal spatial omics data · Penn · PhD

Lost to a baseline

Spatial PCA was beaten in cross-validated MSPE by RapPCA (20.56 vs 46.09, and 49.90 for built-in) and in TMSE (271.35 vs 292.78, and 296.59 for built-in) on HER2+ breast tumor spatial transcriptomics data

Statistical Machine Learning for Spatial- and Network-Linked Data · University of Washington · PhD

Tried and failed

validating targeted PCR expression via global RNA-sequencing applied to differential gene expression analysis. Outcome: did not generalise. Reason: significant expression differences detected by ddPCR were not detected as differentially expressed in global transcriptomic sequencing

Examining the role of metabolism in CD4+ T cell-driven inflammation · UT Austin · PhD

Tried and failed

cross-platform validation of transcriptomics with proteomics applied to differential gene expression analysis. Outcome: did not generalise. Reason: significant mRNA differential expression failed to correlate with corresponding protein abundance changes in proteomic assays

Genetic Expression Assessment of N6-Methyladenosine (m6A) in Relation to Migraine Remission After Pregnancy · Harvard · Master's

Lost to a baseline

CeNGEN single-cell RNA sequencing profiling did not correlate well with in vivo transcriptional/translational reporter gene expression levels across glr-1 neurons

Behavior-Coupled Neural Circuit Analysis of Chemosensory Responses in C. elegans · Georgia Tech · PhD

Tried and failed

intracellular flow cytometry for surface/secreted protein applied to antigen-specific T cell protein expression validation. Outcome: no signal. Reason: Protein level changes were undetectable despite strong transcriptomic upregulation

Mechanisms of T cell dysfunction during human T cell leukemia virus type 1 infection · Imperial · PhD

Tried and failed

spatial transcriptomics for differential gene expression applied to spinal tissue sections. Outcome: no signal. Reason: candidate gene downregulation identified by spatial sequencing was not confirmed by single-molecule in situ hybridization

Exploring genetic interactions in adolescent idiopathic scoliosis · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected the use of reference-free cell type deconvolution algorithms (e.g., Zaitsev et al. 2019) on bulk transcriptomics due to significantly inferior performance compared to reference-based methods.

An integrated immune -omic approach to unravel the sepsis response · Oxford · PhD

Tried and failed

bulk RNA sequencing applied to detecting cell-type-specific differential expression. Outcome: no signal. Reason: cell-type-specific transcriptional signals were diluted and obscured within heterogeneous tissue samples

Dissecting cellular heterogeneity in human pathologies using single cell genomics · UT Austin · PhD

Tried and failed

bulk transcriptomic profiling applied to detecting subclonal relapse pathway enrichment. Outcome: no signal. Reason: signal from rare subclonal populations was diluted and masked in bulk cell population averages

Dissecting cellular heterogeneity in human pathologies using single cell genomics · UT Austin · PhD

Tried and failed

bulk RNA sequencing after mild behavioral stimulation applied to heterogeneous brain tissue. Outcome: no signal. Reason: Cellular heterogeneity and mild stimulus strength diluted transcriptional changes below detection thresholds

Epigenetic memory aids: Synaptic and molecular effects of HDAC inhibition that support memory formation · EPFL · PhD

Considered and rejected

Considered and rejected: Rejected whole mRNA sequencing in favor of targeted capture panels due to high mRNA sequencing cost, read-depth dominance by non-disease transcripts, and insufficient splice junction depth.

Clinical utility of targeted RNAseq in neuromuscular and immune disorders · Georgia Tech · PhD

Lost to a baseline

A considerable number of tissue samples failed RNA quality control criteria prior to small RNA library preparation and were excluded from final microRNA sequencing.

Investigating Klothos and microRNAs as potential novel prognostic markers and therapeutic targets for Human Breast Cancer (HBC) and Canine Mammary Carcinoma (CMC) · Texas Tech · PhD

Lost to a baseline

Single-cell transcriptomes with >5% mitochondrial counts, >4% dissociation signature read fraction, or >40,000 unique RNA features excluded during scRNA-seq quality control.

CROSS-COMPARTMENT IMMUNOLOGY OF PEDIATRIC NEUROINFLAMMATORY DISEASE · Penn · PhD

Lost to a baseline

7 patient samples (out of 16 sorted pairs) excluded from paired transcriptomic analysis due to low macrophage cell yield (<5000 cells), inadequate RNA content normalisation factor, or outlier status on principal component analysis.

Characterising Mer and Axl receptor tyrosine kinase expression and transcriptomic profiling of myeloid cells in hepatocellular carcinoma · Imperial · PhD

Tried and failed

post-perturbation comparative transcriptomic profiling applied to host response during viral infection. Outcome: no signal. Reason: pathogen-induced global shutdown of host gene expression degraded baseline comparison transcripts

Mapping variable host gene expression states to viral infection · Penn · PhD

Tried and failed

immunoprecipitation combined with nascent RNA sequencing applied to insect cell line transcription mapping. Outcome: no signal. Reason: overwhelming majority of reads mapped to contaminating cytoplasmic viral RNA polymerases instead of host genome

LIVE-CELL ANALYSIS OF POL II AND INTERACTING FACTOR-DYNAMICS CONSTRAINS MECHANISMS OF POL II PAUSE RELEASE AND PRODUCTIVE ELONGATION · Cornell · PhD

Left open

Validate RNA-seq transcriptomic findings using secondary assays such as qPCR or in situ hybridization. Blocker: Requires wet lab facilities, tissue samples, and experimental assays (qPCR or in situ hybridization)

Mechanisms Driving the Skeletal Response to Mechanical Loading and Parathyroid Hormone · Cornell · PhD

Left open

Validate candidate genes from the transcriptomics study, such as AHSP and TFR2, using qPCR. Blocker: Requires a wet lab, biological samples, and qPCR equipment.

Muscle wasting in older adults · Imperial · Doctorate

Left open

Validate inferCNV-derived subclonal copy number variation profiles from single-nucleus RNA sequencing using matched patient whole-genome sequencing data. Blocker: Requires matched patient whole-genome sequencing data from the private tumor cohort or wet-lab sequencing of patient samples

Multiomic insights into gastroenteropancreatic neuroendocrine tumor biology · Harvard · PhD

Left open

Integrate single-cell RNA-seq or spatial transcriptomics to validate ARF-induced gene expression changes in specific cell types. Blocker: Requires wet-lab experiments to generate or validate single-cell/spatial transcriptomics data on engineered plant lines

Decoding the Transcriptional Specificity of Auxin Signaling: A Synthetic Biology Approach · Virginia Tech · Doctorate

Left open

Use optimized triple-reporter flow cytometry to profile the transcriptome and chromatin of switched IFNg+ Th1-like cells. Blocker: Requires wet lab facilities, specific reporter mice/cells, flow cytometry cell sorting, and sequencing infrastructure.

INTERLEUKIN 2 INDUCIBLE T CELL KINASE (ITK) FINE TUNES T CELL DIFFERENTIATION · Cornell · PhD

Left open

Disentangle lineage relationships between early effector and chronic exhausted CD8+ T cell subsets using scRNA-seq trajectory analysis, spatial transcriptomics, or CRISPR barcoding. Blocker: Requires wet lab experimentation involving single-cell spatial transcriptomics, single-cell RNA-seq, or CRISPR barcoding in infection models.

Cell Intrinsic Factors Influence CD8+ T Cell Fate Decisions Following Acute and Chronic Infection · Cornell · PhD

Left open

Assess cellular resource competition effects on nuclear import/export, folding, and post-translational machinery using transcriptomic and proteomic profiling. Blocker: Requires wet lab experimental generation of transcriptomic and proteomic data from mammalian cell cultures.

Resource-aware mammalian cell engineering · Imperial · PhD

Left open

Evaluate and characterize non-dichotomized B cell helper T cell populations beyond classical Tfh and Tph using single-cell RNA sequencing data. Blocker: Requires murine wet-lab experiments, FACS sorting, and sequencing or access to unpublished raw single-cell sequencing datasets.

Peripheral T helper Cells in a Murine Chronic Inflammation Model · Harvard · Master's

Left open

Perform low-input RNA-sequencing and transcriptomic analysis on sorted GFP+ versus mCherry+ disseminated tumor cells from Jedi mice. Blocker: Requires wet-lab sorting of mouse tissue and low-input RNA sequencing experimental protocols.

Characterization of Immune Evasion Mechanisms of Breast Cancer Disseminated Cells in the Lung · Harvard · Master's

Left open

Perform spatial and single-cell transcriptomics on PLD-high and PLD-low xenograft cells sorted by IMPACT to assess metastasis capability. Blocker: Requires wet lab facilities, animal xenograft models, cell sorting via IMPACT, and transcriptomic sequencing apparatus

PHOTOAFFINITY LABELING STRATEGIES TO STUDY PHOSPHOLIPASE D SIGNALING · Cornell · PhD