Kimberly Verniers

Background: Long non-coding RNAs (lncRNAs) are numerous, often lowly expressed, and highly context-dependent, making their functional characterization challenging using single-readout approaches. Integrating target repression assessment with complementary molecular and cellular phenotyping is therefore important for systematic lncRNA functional characterization across cellular contexts. Methods: To address this, we developed an arrayed CRISPR interference (CRISPRi)-based multimodal framework for functional lncRNA characterization across cellular contexts, pairing targeted knockdown with target repression assessment, shallow 3′-end RNA-seq, live-cell growth analysis, and Cell Painting-based morphological profiling. As a proof of concept, we applied this strategy to HEK293T and KELLY cells, targeting 68 expressed lncRNAs. Results: Targeted 3′-end RNA capture-seq stratified targets by baseline coverage and confirmed measurable repression for 18 lncRNAs in HEK293T cells and 21 lncRNAs in KELLY cells. Transcriptomic profiling captured the broadest layer of downstream response to lncRNA repression, whereas growth and morphology phenotypes were less frequent and restricted to a small subset of targets. Matched comparisons between HEK293T and KELLY showed that both molecular and cellular responses to the same lncRNA perturbation were often cell-line-dependent. As a representative multimodal hit, EPB41L4A-AS1 repression in KELLY cells was associated with reduced translation- and biosynthesis-related pathway activity, reduced cell growth, and morphological feature changes involving Golgi, actin and mitochondria. Conclusions: Together, these results demonstrate that arrayed CRISPRi coupled with multimodal profiling provides a structured framework for systematically characterizing lncRNA perturbation effects across complementary molecular and cellular readouts and prioritizing candidate functional lncRNAs for downstream mechanistic studies.

Background Circulating nucleic acids in blood plasma form an attractive, minimally invasive resource to study human health and disease. In this study, we aimed to identify cell-free RNA alterations that can distinguish cancer patients from cancer-free individuals. Methods We first performed mRNA capture sequencing on 266 blood plasma samples from cancer patients and controls, including a discovery set of 208 donors across 25 cancer types and a replication set of 58 donors across three cancer types. We first conducted group-level comparisons and then compared individual patient profiles to a reference control population in a one-versus-many approach. This approach was further evaluated in independent cohorts: a prostate cancer plasma cohort (n = 180), a non-malignant disease plasma cohort (n = 125), a lymphoma plasma cohort (n = 65), and a bladder cancer urine cohort (n = 24), each including both patients and controls. Results Here we show that cancer patients exhibit both cancer type-specific and general cell-free RNA alterations. However, differentially abundant RNAs vary widely among patients and across cohorts, hampering robust biomarker identification. By comparing individual patient profiles to control populations, we identify so-called biomarker tail genes, which strongly deviate from controls. The number of these genes per sample distinguishes cancer patients from control samples. Independent cohorts also confirm the potential of this approach. Conclusions Our findings demonstrate substantial heterogeneity in cell-free RNA alterations among cancer patients and propose that patient-specific changes can be exploited for classification.

Neuroblastoma is a pediatric cancer of the sympathetic nervous system characterized by heterogeneous cell states that mirror normal differentiation trajectories. Each state is governed by a core regulatory transcriptional circuitry that reinforces cell identity through an autoregulatory feedforward loop. We identified the long non-coding RNA NESPR as specifically expressed in adrenergic neuroblastoma cells. NESPR expression correlates with high-risk neuroblastoma clinical parameters and poor patient survival. NESPR is located within an insulated gene neighborhood alongside PHOX2B, a master transcription factor of the adrenergic identity. NESPR depletion reduced cell proliferation and increased caspase activity in neuroblastoma cell lines, and NESPR knockout in a neuroblastoma zebrafish model led to reduced tumor penetrance. Subcellular localization revealed NESPR to be a cytosolic long non-coding RNA, suggesting a trans -regulatory function. RNA-sequencing following NESPR depletion revealed a shift from an adrenergic to a mesenchymal cell state, due to proteotoxic stress-induced molecular reprogramming. These findings suggest NESPR as a regulator of neuroblastoma cell identity with potential therapeutic opportunities in high-risk neuroblastoma cases.

It is generally assumed that extracellular long RNA molecules in biofluids are fragmented. Few studies have indirectly hinted at the existence of possibly functional, intact long RNA transcripts. In search for such RNA molecules, we developed a long-read full transcript sequencing workflow for low-input and low-quality samples. We applied our method to human blood plasma, urine, and their isolated macromolecular compartments, in parallel with total RNA sequencing. This approach enabled us to find intact messenger RNA molecules in human biofluids and macromolecular compartments. We showed that the full-length transcriptome of human urine and blood plasma differs, but we also reveal intact messenger RNA molecules shared between biofluids. In addition, we show that these intact molecules are differentially distributed over fractionated macromolecular compartments. This study provides a foundation for future extracellular RNA studies to elucidate the human biofluid full-length transcriptome.

The potential of RNA-based liquid biopsy is increasingly being recognized in diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin’s lymphoma. This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n = 171) derived from a DLBCL PDTX model, including 27 humanized (HIS) PDTX, 8 HIS non-PDTX, and 21 non-HIS PDTX non-obese diabetic (NOD)-scid IL2Rgnull (NSG) mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), CD20-targeted human IFNα2-based AcTaferon combined with CHOP (huCD20-Fc-AFN-CHOP), or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform. RNA sequencing data of the formalin-fixed paraffin-embedded (FFPE) tissue and blood plasma samples of the original patient were included. Flow cytometry was performed on immune cells isolated from whole blood, spleen, and bone marrow. Bulk deconvolution was performed using the Tabula Sapiens v1 basis matrix. Both R-CHOP and huCD20-Fc-AFN-CHOP were able to control tumor growth in most mice. Xenograft tumor volume was strongly associated with circulating tumor RNA (ctRNA) concentration (p < 0.001, R = 0.89), as well as with the number of detected human genes (p < 0.001, R = 0.79). Abundance analysis identified tumor-specific biomarkers that were dynamically tracked during tumor growth or treatment. An 8-gene signature demonstrated high accuracy for assessing therapy response (AUC 0.92). The tumoral gene detectability in the ctRNA of the PDTX-derived plasma was associated with RNA abundance levels in the patient’s tumor tissue and blood plasma (p < 0.001), confirming that tumoral gene abundance contributes to the cell-free RNA (cfRNA) profile. Decomposing the transcriptome, however, revealed high inter- and intra-mouse variability, which was lower in the HIS PDTX mice, indicating an impact of human engraftment on the stability and profile of cfRNA. Immunochemotherapy resulted in B cell depletion, and tumor clearance was reflected by a decrease in the fraction of human CD45+ cells. Lastly, bulk deconvolution provided complementary biological insights into the composition of the tumor and circulating immune system. In conclusion, the blood plasma-derived transcriptome serves as a biomarker source in a preclinical PDTX model, enables the assessment of biological pathways, and enhances the understanding of cfRNA dynamics.

Extracellular RNA (cell‐free RNA; exRNA) from blood‐derived liquid biopsies is an appealing, minimally invasive source of disease biomarkers. As pre‐analytical variables strongly influence exRNA measurements, their reporting is essential for meaningful interpretation and replication of results. The aim of this review was to chart to what extent pre‐analytical variables are documented, to pinpoint shortcomings and to improve future reporting. In total, 200 blood plasma exRNA studies published in 2018 or 2023 were reviewed for annotation of 22 variables associated with blood collection, plasma preparation, and RNA purification. Our results show that pre‐analytical variables are poorly documented, with only three out of 22 variables described in over half of the publications. The percentage of variables reported ranged from 4.6% to 54.6% (mean 24.84%) in 2023 and from 4.6% to 57.1% (mean 28.60%) in 2018. Recommendations and guidelines (i.e., BRISQ, ASCO‐CAP, BloodPAC, PPMPT, and CEN standards) have currently not resulted in improved reporting. In conclusion, our results highlight the lack of reporting pre‐analytical variables in exRNA studies and advocate for a consistent use of available standards, endorsed by funders and journals.

Circulating nucleic acids in blood plasma form an attractive resource to study human health and disease. Here, we applied mRNA capture sequencing of blood plasma cell-free RNA from 266 cancer patients and cancer-free controls (discovery n = 208, 25 cancer types; replication n = 58, 3 types). We observed cancer type-specific as well as pan-cancer alterations in cell-free transcriptomes compared to controls. Differentially abundant RNAs were heterogenous among patients and among cohorts, hampering identification of robust cancer biomarkers. Therefore, we developed a novel method that compares each individual cancer patient to a reference control population to identify so-called biomarker tail genes. The number of biomarker tail genes in a sample enables distinguishing an individual cancer patient from controls. The potential of this novel approach was confirmed in additional cohorts of 65 plasma donors (2 lymphoma types) and 24 urine donors (bladder cancer). Together, our findings demonstrate heterogeneity in cell-free RNA alterations among cancer patients and propose that case-specific alterations can be exploited for classification purposes.

We assess the performance of mRNA capture sequencing to identify fusion transcripts in FFPE tissue of different sarcoma types, followed by RT-qPCR confirmation. To validate our workflow, six positive control tumors with a specific chromosomal rearrangement were analyzed using the TruSight RNA Pan-Cancer Panel. Fusion transcript calling by FusionCatcher confirmed these aberrations and enabled the identification of both fusion gene partners and breakpoints. Next, whole-transcriptome TruSeq RNA Exome sequencing was applied to 17 fusion gene-negative alveolar rhabdomyosarcoma (ARMS) or undifferentiated round cell sarcoma (URCS) tumors, for whom fluorescence in situ hybridization (FISH) did not identify the classical pathognomonic rearrangements. For six patients, a pathognomonic fusion transcript was readily detected, i.e., PAX3-FOXO1 in two ARMS patients, and EWSR1-FLI1, EWSR1-ERG, or EWSR1-NFATC2 in four URCS patients. For the 11 remaining patients, 11 newly identified fusion transcripts were confirmed by RT-qPCR, including COPS3-TOM1L2, NCOA1-DTNB, WWTR1-LINC01986, PLAA-MOB3B, AP1B1-CHEK2, and BRD4-LEUTX fusion transcripts in ARMS patients. Additionally, recurrently detected secondary fusion transcripts in patients diagnosed with EWSR1-NFATC2-positive sarcoma were confirmed (COPS4-TBC1D9, PICALM-SYTL2, SMG6-VPS53, and UBE2F-ALS2). In conclusion, this study shows that mRNA capture sequencing enhances the detection rate of pathognomonic fusions and enables the identification of novel and secondary fusion transcripts in sarcomas.

RNA sequencing has become the gold standard for transcriptome analysis but has an inherent limitation of challenging quantification of low-abundant transcripts. In contrast to microarray technology, RNA sequencing reads are proportionally divided in function of transcript abundance. Therefore, low-abundant RNAs compete against highly abundant - and sometimes non-informative - RNA species. We developed an easy-to-use strategy based on high-affinity RNA-binding oligonucleotides to block reverse transcription and PCR amplification of specific RNA transcripts, thereby substantially reducing their abundance in the final sequencing library. To demonstrate the broad application potential of our method, we applied it to different transcripts and library preparation strategies, including YRNAs in small RNA sequencing of human blood plasma, mitochondrial rRNAs in both 3’ end sequencing and long-read sequencing, and MALAT1 in single-cell 3’ end sequencing. We demonstrate that the blocking strategy is highly efficient, reproducible, specific, and generally results in better transcriptome coverage and complexity. Furthermore, our method does not require modifications of the library preparation procedure apart from simply adding blocking oligonucleotides to the RT reaction and can thus be easily integrated into virtually any RNA sequencing library preparation protocol.

Molecular phenotyping through shallow 3’-end RNA-sequencing workflows is increasingly applied in the context of large-scale chemical or genetic perturbation screens to study disease biology or support drug discovery. While these workflows enable accurate quantification of the most abundant genes, they are less effective for applications that require expression profiling of low abundant transcripts, like long non-coding RNAs (lncRNAs), or selected gene panels. To tackle these issues, we describe a workflow combining 3’-end library preparation with 3’-end hybrid capture probes and shallow RNA-sequencing for cost-effective, targeted quantification of subsets of (low abundant) genes across hundreds to thousands of samples. To assess the performance of the method, we designed a capture probe set for more than 100 mRNA and lncRNA target genes and applied the workflow to a cohort of 360 samples. When compared to standard 3’-end RNA-sequencing, 3’-end capture sequencing resulted in a more than 100-fold enrichment of target gene abundance while conserving relative inter-gene and inter-sample abundances. 3’-end RNA capture sequencing enables accurate targeted gene expression profiling at extremely shallow sequencing depth.