Abstract:Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude … Show more
“…12 ), comparable to the results of previous comparisons of different RNA-Seq sequencing approaches (e.g. Prime-Seq vs. True-Seq: R 2 = 0.64) 20 , suggesting that the Combi-Seq approach does not introduce major biases during library preparation steps. Fig.…”
Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we present a scalable microfluidic workflow, called Combi-Seq, to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devise a deterministic combinatorial DNA barcoding approach to encode treatment conditions, enabling the gene expression-based readout of drug effects in a highly multiplexed fashion. We apply Combi-Seq to screen the effect of 420 drug combinations on the transcriptome of K562 cells using only ~250 single cell droplets per condition, to successfully predict synergistic and antagonistic drug pairs, as well as their pathway activities.
“…12 ), comparable to the results of previous comparisons of different RNA-Seq sequencing approaches (e.g. Prime-Seq vs. True-Seq: R 2 = 0.64) 20 , suggesting that the Combi-Seq approach does not introduce major biases during library preparation steps. Fig.…”
Anti-cancer therapies often exhibit only short-term effects. Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations. Identification of the right combination is challenging and would benefit from high-content, high-throughput combinatorial screens directly on patient biopsies. However, such screens require a large amount of material, normally not available from patients. To address these challenges, we present a scalable microfluidic workflow, called Combi-Seq, to screen hundreds of drug combinations in picoliter-size droplets using transcriptome changes as a readout for drug effects. We devise a deterministic combinatorial DNA barcoding approach to encode treatment conditions, enabling the gene expression-based readout of drug effects in a highly multiplexed fashion. We apply Combi-Seq to screen the effect of 420 drug combinations on the transcriptome of K562 cells using only ~250 single cell droplets per condition, to successfully predict synergistic and antagonistic drug pairs, as well as their pathway activities.
“…RNA concentration and quality were checked using a high-sensitivity RNA chip on an Agilent Bioanalyzer. RNA sequencing was executed applying an adapted version of the prime-seq protocol [ 51 ], with some adjustments as described by Chavarria-Pizarro et al [ 41 ]. We used the same protocol for cleaning and the same residual primers and preamplification as in Chavarria-Pizarro et al [ 41 ] and we obtained the same final reaction volume of 50 µL, which was further amplified, cleaned and quantified using the protocol of Janjic et al [ 51 ] (for more details see [ 41 ]).…”
Section: Methodsmentioning
confidence: 99%
“…RNA sequencing was executed applying an adapted version of the prime-seq protocol [ 51 ], with some adjustments as described by Chavarria-Pizarro et al [ 41 ]. We used the same protocol for cleaning and the same residual primers and preamplification as in Chavarria-Pizarro et al [ 41 ] and we obtained the same final reaction volume of 50 µL, which was further amplified, cleaned and quantified using the protocol of Janjic et al [ 51 ] (for more details see [ 41 ]). The cDNA libraries containing 96 barcoded samples were single-end sequenced on four lanes of a high-output flow cell on the HiSeq 1500 platform (Illumina) at the Laboratory for Functional Genome Analysis (LAFUGA) of Gene Center Munich, LMU Munich.…”
Antibiotics are primarily found in the environment due to human activity, which has been reported to influence the structure of biotic communities and the ecological functions of soil and water ecosystems. Nonetheless, their effects in other terrestrial ecosystems have not been well studied. As a result of oxidative stress in organisms exposed to high levels of antibiotics, genotoxicity can lead to DNA damage and, potentially, cell death. In addition, in symbiotic organisms, removal of the associated microbiome by antibiotic treatment has been observed to have a big impact on the host, e.g., corals. The lung lichen Lobaria pulmonaria has more than 800 associated bacterial species, a microbiome which has been hypothesized to increase the lichen’s fitness. We artificially exposed samples of L. pulmonaria to antibiotics and a stepwise temperature increase to determine the relative effects of antibiotic treatments vs. temperature on the mycobiont and photobiont gene expression and the viability and on the community structure of the lichen-associated bacteria. We found that the mycobiont and photobiont highly reacted to different antibiotics, independently of temperature exposure. We did not find major differences in bacterial community composition or alpha diversity between antibiotic treatments and controls. For these reasons, the upregulation of stress-related genes in antibiotic-treated samples could be caused by genotoxicity in L. pulmonaria and its photobiont caused by exposure to antibiotics, and the observed stress responses are reactions of the symbiotic partners to reduce damage to their cells. Our study is of great interest for the community of researchers studying symbiotic organisms as it represents one of the first steps to understanding gene expression in an endangered lichen in response to exposure to toxic environments, along with dynamics in its associated bacterial communities.
“…We processed the 240 striatal punch biopsies (40 mice x 3 regions x two hemispheres) using the primeseq protocol as described (Janjic, et al, 2022). In this plate-based bulk RNA-seq protocol, RNA is purified directly from the RLT dissolved biopsies using magnetic beads and subsequently reverse transcribed with barcoded oligo-dT primers.…”
During cortico-basal ganglia dependent learning, relevant environmental information is associated with certain outcomes; such learning is essential to generate adaptive behaviour in a continuously changing environment. Through repetitive trial-and-error experiences, actions are optimized and cognitive associative load can be relieved through consolidation and automatization. Although the molecular basis of learning is well studied, region-specific genome-wide expression profiles of the striatum, the major input region of cortico-basal ganglia circuits, during learning are lacking. Here we combined an automated operant conditioning paradigm with an efficient RNA-sequencing protocol to compare expression profiles among three learning stages in three striatal regions per hemisphere in a total of 240 striatal biopsies. Notably, the inclusion of matched yoked controls allowed reliably identifying learning-related expression changes. With 593 differently expressed genes (3.3% of all detected genes), we find the strongest effect of learning at an early, goal-directed stage across all three striatal region and identify a total of 921 learning-related expression changes. Our dataset provides a unique resource to study molecular markers of striatal learning.
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