Design and Optimization Methods for Pin-Limited and Cyberphysical Digital Microfluidic Biochips

<p>Microfluidic biochips have now come of age, with applications to biomolecular recognition for high-throughput DNA sequencing, immunoassays, and point-of-care clinical diagnostics. In particular, digital microfluidic biochips, which use electrowetting-on-dielectric to manipulate discrete droplets (or "packets of biochemical payload") of picoliter volumes under clock control, are especially promising. The potential applications of biochips include real-time analysis for biochemical reagents, clinical diagnostics, flash chemistry, and on-chip DNA sequencing. The ease of reconfigurability and software-based control in digital microfluidics has motivated research on various aspects of automated chip design and optimization.</p><p>This thesis research is focused on facilitating advances in on-chip bioassays, enhancing the automated use of digital microfluidic biochips, and developing an "intelligent" microfluidic system that has the capability of making on-line re-synthesis while a bioassay is being executed. This thesis includes the concept of a "cyberphysical microfluidic biochip" based on the digital microfluidics hardware platform and on-chip sensing technique. In such a biochip, the control software, on-chip sensing, and the microfluidic operations are tightly coupled. The status of the droplets is dynamically monitored by on-chip sensors. If an error is detected, the control software performs dynamic re-synthesis procedure and error recovery.</p><p>In order to minimize the size and cost of the system, a hardware-assisted error-recovery method, which relies on an error dictionary for rapid error recovery, is also presented. The error-recovery procedure is controlled by a finite-state-machine implemented on a field-programmable gate array (FPGA) instead of a software running on a separate computer. Each state of the FSM represents a possible error that may occur on the biochip; for each of these errors, the corresponding sequence of error-recovery signals is stored inside the memory of the FPGA before the bioassay is conducted. When an error occurs, the FSM transitions from one state to another, and the corresponding control signals are updated. Therefore, by using inexpensive FPGA, a portable cyberphysical system can be implemented.</p><p>In addition to errors in fluid-handling operations, bioassay outcomes can also be erroneous due the uncertainty in the completion time for fluidic operations. Due to the inherent randomness of biochemical reactions, the time required to complete each step of the bioassay is a random variable. To address this issue, a new "operation-interdependence-aware" synthesis algorithm is proposed in this thesis. The start and stop time of each operation are dynamically determined based on feedback from the on-chip sensors. Unlike previous synthesis algorithms that execute bioassays based on pre-determined start and end times of each operation, the proposed method facilitates "self-adaptive" bioassays on cyberphysical microfluidic biochips.</p><p>Another design problem addressed in this thesis is the development of a layout-design algorithm that can minimize the interference between devices on a biochip. A probabilistic model for the polymerase chain reaction (PCR) has been developed; based on the model, the control software can make on-line decisions regarding the number of thermal cycles that must be performed during PCR. Therefore, PCR can be controlled more precisely using cyberphysical integration.</p><p>To reduce the fabrication cost of biochips, yet maintain application flexibility, the concept of a "general-purpose pin-limited biochip" is proposed. Using a graph model for pin-assignment, we develop the theoretical basis and a heuristic algorithm to generate optimized pin-assignment configurations. The associated scheduling algorithm for on-chip biochemistry synthesis has also been developed. Based on the theoretical framework, a complete design flow for pin-limited cyberphysical microfluidic biochips is presented.</p><p>In summary, this thesis research has led to an algorithmic infrastructure and optimization tools for cyberphysical system design and technology demonstrations. The results of this thesis research are expected to enable the hardware/software co-design of a new class of digital microfluidic biochips with tight coupling between microfluidics, sensors, and control software.</p>Dissertatio

Combining Metabolic Engineering and Synthetic Biology Approaches for the Production of Abscisic Acid in Yeast

Nature presents us with a myriad of complex and diverse molecules. Many of these molecules prove to be useful to humans and find applications as pharmaceuticals, biofuels, agrochemicals, cosmetic ingredients or food additives. One highly promising natural product with a broad range of potential applications is the terpenoid abscisic acid (ABA). ABA fulfils a pivotal role in higher plants by regulating various developmental processes as well as abiotic stress responses. However, ABA is also produced in many other organisms, including humans. It appears to be a ubiquitous and evolutionary conserved signalling molecule throughout nature. Genetically engineered microorganisms, referred to as microbial cell factories, can be a sustainable source of natural products. In this thesis, a cell factory for the heterologous production of ABA was established and optimized employing the yeast Saccharomyces cerevisiae. Cell factory development is an inherently time-consuming process. As an enabling technology for subsequent work on the ABA cell factory, we expanded the modular cloning toolkit for yeast and made it more applicable for common genetic engineering tasks (Paper I). The ABA biosynthetic pathway of Botrytis cinerea was used to construct an ABA-producing S. cerevisiae strain (Paper II). The activity of two B. cinerea proteins, BcABA1 and BcABA2, was found to limit ABA titers. Two optimization approaches were devised for the following studies. Firstly, various rational engineering targets were explored, of which the native yeast gene PAH1 was identified as the most promising candidate (Paper III). Knockdown of PAH1 benefited ABA production without affecting growth. Secondly, platform strains for screening BcABA1 and BcABA2 enzyme libraries were developed, which utilize an ABA biosensor and enable a high throughput screening approach (Paper IV). In this work, we combined metabolic engineering and synthetic biology approaches for the heterologous production of ABA, and furthermore provided tools and insights that will be useful beyond the scope of this project

Statistical power analysis for single-cell RNA-sequencing

RNA-sequencing (RNA-seq) is an established method to quantify levels of gene expression genome-wide. The recent development of single cell RNA sequencing (scRNA-seq) protocols opens up the possibility to systematically characterize cell transcriptomes and their underlying developmental and regulatory mechanisms. Since the first publication on single-cell transcriptomics a decade ago, hundreds of scRNA-seq datasets from a variety of sources have been released, profiling gene expression of sorted cells, tumors, whole dissociated organs and even complete organisms. Currently, it is also the main tool to systematically characterize human cells within the Human Cell Atlas Project. Given its wide applicability and increasing popularity, many experimental protocols and computational analysis approaches exist for scRNA-seq. However, the technology remains experimentally and computationally challenging. Firstly, single cells contain only minute mRNA amounts that need to be reliably captured and amplified for accurate quantification by sequencing. Importantly, the Polymerase Chain Reaction (PCR) is commonly used for amplification which might introduce biases and increase technical variation. Secondly, once the sequencing results are obtained, finding the best computational processing pipeline can be a struggle. A number of comparison studies have already been conducted - esp. for bulk RNA-seq - but usually they deal only with one aspect of the workflow. Furthermore, in how far the conclusions and recommendations of these studies can be transferred to scRNA-seq is unknown. Related to the processing of RNA-sequencing, we investigate the effect of PCR amplification on differential expression analysis. We find that computational removal of duplicates has either a negligible or a negative impact on specificity and sensitivity of differential expression analysis, and we therefore recommend not to remove read duplicates by mapping position. In contrast, if duplicates are identified using unique molecular identifiers (UMIs) tagging RNA molecules, both specificity and sensitivity improve. The first integral step of any scRNA-seq experiment is the preparation of sequencing libraries from the cells. We conducted an independent benchmarking study of popular library preparation protocols in terms of detection sensitivity, accuracy and precision using the same mouse embryonic stem cells and exogenous mRNA spike-ins. We recapitulate our previous finding that technical variance is markedly decreased when using UMIs to remove duplicates. In order to assign a monetary value to the detected amounts of technical variance, we developed a simulation framework, that enabled us to compare the power to detect differentially expressed genes across the scRNA-seq library preparation protocols. Our experiences during this comparison study led to the development of the sequencing data processing in zUMIs and the simulation framework and power analysis in powsimR. zUMIs is a pipeline for processing scRNA-seq data with flexible choices regarding UMI and cell barcode design. In addition, we showed with powsimR simulations that the inclusion of intronic reads for gene expression quantification increases the power to detect DE genes and added it as a unique feature to zUMIs. In powsimR, we present our simulation framework extending choices concerning data analysis, enabling researchers to assess experimental design and analysis plans of RNA-seq in terms of statistical power. Lastly, we conducted a systematic evaluation of scRNA-seq experimental and analytical pipelines. We found that choices made concerning normalisation and library preparation protocols have the biggest impact on the validity of scRNA-seq DE analysis. Choosing a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the cell sample size. Taken together, we have established and applied a simulation framework that allowed us to benchmark experimental and computational scRNA-seq protocols and hence inform the experimental design and method choices of this important technology

Improving & applying single-cell RNA sequencing

The cell is the fundamental building block of life. With the advent of single-cell RNA sequencing (scRNA-seq), we can for the first time assess the transcriptome of many individual cells. This has profound implications for biological and medical questions and is especially important to characterize heterogeneous cell populations and rare cells. However, the technology is technically and computationally challenging as complementary DNA (cDNA) needs to be generated and amplified from minute amounts of mRNA and sequenceable libraries need to be efficiently generated from many cells. This requires to establish different protocols, identify important caveats, benchmark various methods and improve them if possible. To this end, we analysed amplification bias and its effect on detecting differentially expressed genes in several bulk and a single-cell RNA sequencing methods. We found that correcting for amplification bias is not possible computationally but improves the power of scRNA-seq considerably, though neglectable for bulk-RNA-seq. In the second study we compared six prominent scRNA-seq protocols as more and more single-cell RNA-sequencing are becoming available, but an independent benchmark of methods is lacking. By using the same mouse embryonic stem cells (mESCs) and exogenous mRNA spike-ins as common reference, we compared six important scRNA-seq protocols in their sensitivity, accuracy and precision to quantify mRNA levels. In agreement with our previous study, we find that the precision, i.e. the technical variance, of scRNA-seq methods is driven by amplification bias and drastically reduced when using unique molecular identifiers to remove amplification duplicates. To assess the combined effects of sensitivity and precision and to compare the cost-efficiency of methods we compared the power to detect differentially expressed genes among the tested scRNA-seq protocols using a novel simulation framework. We find that some methods are prohibitively inefficient and others show trade-offs depending on the number of cells per sample that need to be analysed. Our study also provides a framework for benchmarking further improvements of scRNA-seq protocol and we published an improved version of our simulation framework powsimR. It uniquely recapitulates the specific characteristics of scRNA-seq data to enable streamlined simulations for benchmarking both wet lab protocols and analysis algorithms. Furthermore, we compile our experience in processing different types of scRNA-seq data, in particular with barcoded libraries and UMIs, and developed zUMIs, a fast and flexible scRNA-seq data processing software overcoming shortcomings of existing pipelines. In addition, we used the in-depth characterization of scRNA-seq technology to optimize an already powerful scRNA-seq protocol even further. According to data generated from exogenous mRNA spike-ins, this new mcSCRB-seq protocol is currently the most sensitive scRNA-seq protocol available. Single-cell resolution makes scRNA-seq uniquely suited for the understanding of complex diseases, such as leukemia. In acute lymphoblastic leukemia (ALL), rare chemotherapy-resistant cells persist as minimal residual disease (MRD) and may cause relapse. However, biological mechanisms of these relapse-inducing cells remain largely unclear because characterisation of this rare population was lacking so far. In order to contribute to the understanding of MRD, we leveraged scRNA-seq to study minimal residual disease cells from ALL. We obtained and characterised rare, chemotherapy-resistant cell populations from primary patients and patient cells grown in xenograft mouse models. We found that MRD cells are dormant and feature high expression of adhesion molecules in order to persist in the hematopoietic niche. Furthermore, we could show that there is plasticity between resting, resistant MRD cells and cycling, therapy-sensitive cells, indicating that patients could benefit from strategies that release MRD cells from the niche. Importantly, we show that our data derived from xenograft models closely resemble rare primary patient samples. In conclusion, my work of the last years contributes towards the development of experimental and computational single-cell RNA sequencing methods enabling their widespread application to biomedical problems such as leukemia

Photon-Upconversion Nanoparticles as Background-Free Luminescent Labels for Immunoanalytical Applications

This thesis builds on photon-upconversion nanoparticles (UCNPs) as background-free luminescent labels in bioaffinity assays using antibodies as recognitions elements. UCNPs are nanocrystals that can absorb two or more near-infrared (NIR) photons and emit light with higher energy (anti-Stokes emission). The NIR excitation drastically reduces the measurement background by avoiding autofluorescence and minimizing light scattering. Together with a high photostability and constant emission (no blinking), UCNPs have become an excellent label for many bioanalytical applications. The aim of this work was to develop UCNP-based assays with the possibility to perform a single-molecule (digital) readout. The first part of the thesis describes the development of immunoassays from a historical perspective and explains the fundamental building blocks needed for affinity assays. Various assay formats are described. The structure, function, and preparation of antibodies is explained. Alternative recognition labels like aptamers and molecularly imprinted polymers (MIPs) are critically discussed, and important label types are examined in detail. Cornerstones in the immunoassay development are highlighted using selected examples from the literature. The definition, advantages, and challenges of digital (single-molecule) affinity assays are discussed with respect to different label types, such as enzymes, small molecular labels, and nanoparticles. The first research article describes the development of an immunoassay for counting individual molecules of the cancer biomarker prostate-specific antigen (PSA) with UCNPs coupled to an anti-PSA antibody. Individual UCNPs bound to a PSA molecule were visualized using a modified epifluorescence microscope that was equipped with a 980 nm-laser. The PSA concentration was determined in a digital way by counting the number of luminescent spots visible in a defined area of the microplate. Synthesis of the detection conjugate was optimized with respect to minimizing the aggregation of the nanoparticles, and the quality was controlled using agarose gel electrophoresis. The digital upconversion-linked immunosorbent assay (ULISA) reached a low limit of detection (LOD) of 1.2 pg/mL (42 fM) and covered three orders of magnitude for PSA spiked in 25% blood serum, which was approximately 10× more sensitive than commercial ELISA kits. It was demonstrated that the digital readout is superior to the conventional analog readout of the same microtiter plate using a plate reader equipped with a 980 nm-laser, which resulted in an LOD of 20.3 pg/mL (700 fM, 17× higher LOD). A combination of both readout methods increased the working range to four orders of magnitude from 1 pg/mL to 10,000 pg/mL. The compatibility with standard microplate assay procedures and the high sensitivity make the ULISA a powerful alternative to existing assays and will have a substantial impact in the future. The second research article focused on the surface modification of UCNPs to reduce non-specific binding, while simultaneously increasing the sensitivity of the PSA detection by exploiting the strong affinity of streptavidin towards biotin. A linker consisting of neridronate, a bisphosphonate that strongly coordinates to lanthanide ions, was chosen to anchor a long polyethylene glycol (PEG) spacer with an incorporated alkyne group at the other end. The alkyne group was used for the covalent immobilization of streptavidin azide onto the UCNPs, via a copper-catalyzed click reaction. Like the ULISA with antibody-UCNP conjugates, the digital ULISA with streptavidin-PEG-UCNPs improved the analog readout by a factor of 16. The strong affinity between biotin and streptavidin led to a 50× higher sensitivity compared to the former assay, which led to a subfemtomolar LOD of 800 aM (~50,000 PSA molecules in 100 µL sample) in 25% blood serum. The results obtained for real patient samples were in excellent agreement with results obtained from a standard method based on electrochemiluminescence (Elecsys, Roche). In Research article 3, the unique photophysical properties of UCNPs were exploited for the immunochemical labeling of a cancer marker on the surface of cells. We demonstrated that UCNP labeling is compatible with standard fluorescence labeling techniques but achieves unsurpassed signal to background ratios. We designed and characterized three different SA-UCNP conjugates and compared the results with a standard fluorescence-based readout using SA conjugated to 5(6)-carboxyfluorescein (SA-FAM). It was found that our previously established SA-PEG-neridronate-UCNPs showed the highest specific binding and, at the same time, the lowest non-specific signal among the three tested SA-UCNP conjugates. The signal-to-background ratio of SA-PEG-neridronate-UCNPs was 319, a 50-fold increase compared to the SA-FAM conjugate (signal to background of 6). Control experiments confirmed the specificity of the UCNP staining. The results demonstrated that UCNPs are a valuable addition to the existing repertoire of immunochemical labeling techniques. Research article 4 focuses on the analysis of enzyme kinetics at the single-molecule level. This research is closely related to the digital immunoassay established by the company Quanterix (Chapter IV.6.2). The conventional transition state theory (TST) is used to analyze and explain the reaction rates of enzymes. However, it does not account for static heterogeneity and dynamic effects in proteins, revealed by single-molecule measurements. We analyzed the reaction rates of individual β-D-galactosidase (GAL) and β-D-glucuronidase (GUS) molecules in large arrays of femtoliter-sized wells, revealing a static heterogeneity. The reaction rate distributions gave access to the intrinsic distributions of the free energy of activation (ΔG‡) of GAL and GUS. A broader distribution of ΔG‡ was found for GAL than for GUS, which is potentially caused by the multiple catalytic reaction pathways of GAL as a hydrolase and transglycosylase. Different catalytic reactions of GAL require more catalytically potent conformations for individual enzyme molecules in the enzyme population compared to GUS. Reaction rates of single enzyme molecules do not change over time (10 min). This indicates that each enzyme molecule has a broader set of conformations than it can access during catalysis. We adapted the TST for these findings by assuming transition state ensembles that can not only drive the enzymatic catalysis but also channel the reaction pathway. The aim of this thesis was to employ UCNPs as labels for highly sensitive immunoassays. With the first two research articles, the digital ULISA was successfully introduced and set the foundation for a new generation of digital immunoassays. It was further shown that UCNPs are exceptional labels for the immunochemical labeling of cells. Especially the low background of the UCNP label could have significant impact on tissue diagnostics in the (near) future

Single-molecule protein dynamics during DNA replication

Observing the processes of life that occur in all cellular organisms at the level of single molecules has allowed a deeper understanding of the dynamic processes taking place in complex biological systems. There has been a strong growth in the application of molecular biophysics to visualize in real time the behaviour of single molecules within a reaction, transforming our perception of the molecular processes that occur within a cell. A multitude of proteins participates across the genome to support the processes of replication, transcription, translation, repair and recombination. The continuous interplay of these proteins on the DNA produces unavoidable physical conflicts that have their own impact on genomic stability. Beyond the complexities of the cellular processes that involve DNA as a reaction partner, the duplex is also constantly exposed to DNA-damaging agents as a result of environmental factors such as UV radiation and oxidative stress. It comes as no surprise that replisomes frequently stall and dissociate because of encounters with DNA damage or tightly-bound protein-DNA complexes. In bacteria, such genomic instability can result in the genetic changes that drive antibiotic resistance evolution. Genomic stability is maintained through pathways that ensure continued replication by minimising the frequency or impact of collisions and identifying and repairing stalled forks. The methodologically diverse toolkit of single-molecule biophysics has been used to address a wide range of questions related to complex protein machineries. Specifically, this thesis highlights the application of single-molecule fluorescence methods to visualize and characterize DNA and the proteins that interact with it. In addition, it describes methodological advances that have been made to utilize linear DNA substrates to uncover protein dynamics. The overall goal of the projects described in this thesis was to design protocols and workflows for the production of linear DNA substrates which are (1) easily customizable to adjust for different experimental parameters and (2) which could be utilized to address a diverse range of biological questions, with a key focus on the controlled introduction of specific chemical lesions. This protocol was employed in support of answering a specific question: How do polymerase exchange dynamics affect lesion bypass mechanisms? This thesis focuses on the protein dynamics that occur at the replication fork in the context of roadblocks and lesions. For the first time, we observe replisome collisions with site-specific cyclobutane pyrimidine dimer lesions on linear substrates at the single-molecule level. This assay presents an exciting avenue to unveil further details of replication stalling and restart. Furthermore, this assay can be adapted to introduce a diverse range of roadblocks, to study dynamics of repair proteins at replication forks and observe the behavior of other replisome complexes. Classical biochemical and single-molecule techniques have provided insight into the proteins and macromolecular complexes responsible for rescue of stalled DNA replication forks. While the majority of studies have employed a reductionist approach in focusing on functions of isolated enzymes, recent work has started to explore the reconstitution of multiple-protein complexes of replication and repair pathways on single molecules of DNA. As we gain more knowledge of the dynamics and mechanisms observed at the single-molecule level, we will see emerging a more detailed picture of the molecular steps associated with the rescue stalled forks. This thesis represents an important step towards that more refined understanding

Wide-Field Time-Domain Fluorescence Lifetime Imaging Microscopy (FLIM): Molecular Snapshots of Metabolic Function in Biological Systems.

Steady-state fluorescence imaging is routinely employed to obtain physiological information but is susceptible to artifacts such as absorption and photobleaching. FLIM provides an additional source of contrast oblivious to these but is affected by factors such as pH, gases, and temperature. Here we focused on developing a resolution-enhanced FLIM system for quantitative oxygen sensing. Oxygen is one of the most critical components of metabolic machinery and affects growth, differentiation, and death. FLIM-based oxygen sensing provides a valuable tool for biologists without the need of alternate technologies. We also developed novel computational approaches to improve spatial resolution of FLIM images, extending its potential for thick tissue studies. We designed a wide-field time-domain UV-vis-NIR FLIM system with high temporal resolution (50 ps), large temporal dynamic range (750 ps – 1 μs), short data acquisition/processing times (15 s) and noise-removal capability. Lifetime calibration of an oxygen-sensitive, ruthenium dye (RTDP) enabled in vivo oxygen level measurements (resolution = 8 μM, range = 1 – 300 μM). Combining oxygen sensing with endogenous imaging allowed for the study of two key molecules (NADH and oxygen) consumed at the termini of the oxidative phosphorylation pathway in Barrett’s adenocarcinoma columnar (SEG-1) cells and Esophageal normal squamous cells (HET-1). Starkly higher intracellular oxygen and NADH levels in living SEG-1 vs. HET-1 cells were detected by FLIM and attributed to altered metabolic pathways in malignant cells. We performed FLIM studies in microfluidic bioreactors seeded with mouse myoblasts. For these systems, oxygen concentrations play an important role in cell behavior and gene expression. Oxygen levels decreased with increasing cell densities and were consistent with simulated model outcomes. In single bioreactor loops, FLIM detected spatial heterogeneity in oxygen levels as high as 20%. We validated our calibration with EPR spectroscopy, the gold standard for intracellular oxygen measurements. Differences between FLIM and EPR results were explained by cell lysate-FLIM studies. We proposed a new protocol for estimating oxygen levels by using a reference cell line and cellular lysate analysis. Lastly, we proposed and compared two different image restoration approaches, direct lifetime vs. intensity-overlay. Both approaches improve resolution while maintaining veracity of lifetime.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61675/1/dsud_1.pd

Improving & applying single-cell RNA sequencing

The cell is the fundamental building block of life. With the advent of single-cell RNA sequencing (scRNA-seq), we can for the first time assess the transcriptome of many individual cells. This has profound implications for biological and medical questions and is especially important to characterize heterogeneous cell populations and rare cells. However, the technology is technically and computationally challenging as complementary DNA (cDNA) needs to be generated and amplified from minute amounts of mRNA and sequenceable libraries need to be efficiently generated from many cells. This requires to establish different protocols, identify important caveats, benchmark various methods and improve them if possible. To this end, we analysed amplification bias and its effect on detecting differentially expressed genes in several bulk and a single-cell RNA sequencing methods. We found that correcting for amplification bias is not possible computationally but improves the power of scRNA-seq considerably, though neglectable for bulk-RNA-seq. In the second study we compared six prominent scRNA-seq protocols as more and more single-cell RNA-sequencing are becoming available, but an independent benchmark of methods is lacking. By using the same mouse embryonic stem cells (mESCs) and exogenous mRNA spike-ins as common reference, we compared six important scRNA-seq protocols in their sensitivity, accuracy and precision to quantify mRNA levels. In agreement with our previous study, we find that the precision, i.e. the technical variance, of scRNA-seq methods is driven by amplification bias and drastically reduced when using unique molecular identifiers to remove amplification duplicates. To assess the combined effects of sensitivity and precision and to compare the cost-efficiency of methods we compared the power to detect differentially expressed genes among the tested scRNA-seq protocols using a novel simulation framework. We find that some methods are prohibitively inefficient and others show trade-offs depending on the number of cells per sample that need to be analysed. Our study also provides a framework for benchmarking further improvements of scRNA-seq protocol and we published an improved version of our simulation framework powsimR. It uniquely recapitulates the specific characteristics of scRNA-seq data to enable streamlined simulations for benchmarking both wet lab protocols and analysis algorithms. Furthermore, we compile our experience in processing different types of scRNA-seq data, in particular with barcoded libraries and UMIs, and developed zUMIs, a fast and flexible scRNA-seq data processing software overcoming shortcomings of existing pipelines. In addition, we used the in-depth characterization of scRNA-seq technology to optimize an already powerful scRNA-seq protocol even further. According to data generated from exogenous mRNA spike-ins, this new mcSCRB-seq protocol is currently the most sensitive scRNA-seq protocol available. Single-cell resolution makes scRNA-seq uniquely suited for the understanding of complex diseases, such as leukemia. In acute lymphoblastic leukemia (ALL), rare chemotherapy-resistant cells persist as minimal residual disease (MRD) and may cause relapse. However, biological mechanisms of these relapse-inducing cells remain largely unclear because characterisation of this rare population was lacking so far. In order to contribute to the understanding of MRD, we leveraged scRNA-seq to study minimal residual disease cells from ALL. We obtained and characterised rare, chemotherapy-resistant cell populations from primary patients and patient cells grown in xenograft mouse models. We found that MRD cells are dormant and feature high expression of adhesion molecules in order to persist in the hematopoietic niche. Furthermore, we could show that there is plasticity between resting, resistant MRD cells and cycling, therapy-sensitive cells, indicating that patients could benefit from strategies that release MRD cells from the niche. Importantly, we show that our data derived from xenograft models closely resemble rare primary patient samples. In conclusion, my work of the last years contributes towards the development of experimental and computational single-cell RNA sequencing methods enabling their widespread application to biomedical problems such as leukemia