High-precision quantification of nucleic acids from a predetermined amount of cells using plate-based digital PCR.

Abstract

The present invention relates to a method for quantifying the amount of at least one nucleic acid target, comprising the steps of: a) providing a specified number of cells, wherein the specified number of cells is at least one cell; b) lysing at least one cell, thereby releasing RNA and DNA; c) performing a reverse transcription reaction on the released RNA, thereby generating complementary DNA (cDNA), and further performing a digital polymerase chain reaction (dPCR) on at least one reverse-transcribed RNA target in the cDNA; and / or performing a digital polymerase chain reaction (dPCR) on at least one DNA target in the released DNA, wherein the dPCR is performed in a plate; and d) quantifying the amount of at least one nucleic acid target.

Description

Technical Field

[0001] Field of the Invention The present invention is in the field of molecular biology, particularly in the field of targeted nucleic acid analysis. More specifically, the present invention is in the fields of mRNA and miRNA expression analysis, as well as targeted genomic and mitochondrial copy number polymorphism analysis, which isolate a specific quantity of cells and quantify nucleic acids using plate-based digital PCR.

Background Art

[0002] Background Single-cell analysis enables researchers to uncover new information and gain new insights into the biological processes of individual cells, compared to conventional methods of analyzing cell populations in bulk. Therefore, it is possible to analyze the heterogeneity of individual cells rather than the average output of a cell population.

[0003] Even when cells are identical at the genetic and morphological levels, significant intercellular variability can be observed, especially at the transcriptome level. Capturing this transcriptome heterogeneity is particularly important for research in disease and drug development, where differences in biological responses to drugs, compared between individual cells and tissues, may provide further insights into disease progression or prevention. By understanding the outline of complex transcriptional networks, improved drug response models can be created. In addition to disease and drug screening therapies, single-cell transcript analysis can be applied, for example, particularly in aging, stem cells, gene and cell therapy, prenatal screening, organoid research, and infectious disease research.

[0004] Capturing cellular heterogeneity is also crucial for detecting naturally occurring mutations, chromosomal rearrangements, or copy number variations (CNVs; or copy number mutations (CNAs)) events within cells. CNVs are genomic structural changes (such as deletions, insertions, duplications, translocations, and inversions) that result in an increase or decrease in the copy number of a particular region, ranging from a few base pairs to several hundred base pairs to entire chromosomes. CNVs can be hereditary or the result of de novo somatic cell changes. CNVs account for up to 10–20% of genomic variability and are a source of natural genetic diversity and biological dysfunction in humans. CNVs often result in disruption of gene function, gene quantity imbalance, chromosome end shortening, and positional effects, which are associated with complex diseases and traits such as cancer, obesity, aging, and neurodegenerative and autoimmune diseases. Therefore, quantitative analysis of CNVs at disease-associated loci provides insights into the molecular mechanisms of disease and offers the potential for discovering novel biomarkers.

[0005] Similar to variations in genomic DNA copy number (gDNA CN), changes in mitochondrial DNA copy number (mtDNA CN) in blood or tissues are associated with disease, which is not surprising given the crucial role mitochondria play in cellular homeostasis. In contrast to the constant copy number of genomic DNA (healthy diploid state), significant intercellular variability can be observed at the mitochondrial DNA (mtDNA) level, even when cells are identical in terms of genetic identity and morphology. mtDNA copy number varies and changes between individuals, between different tissues and cells, and even between mitochondria within the same cell. Individual cells can contain hundreds to hundreds of thousands of copies of mtDNA. However, large changes in mtDNA copy number can lead to mitochondrial dysfunction and disease development. Therefore, capturing mtDNA copy number heterogeneity, as with genomic CNVs, is important for disease and drug development research and can be used as an alternative for screening metabolic diseases, cancer, prenatal testing, neurodegenerative diseases, and age-related disorders.

[0006] Furthermore, single-cell CNV analysis can be applied to gene therapy and cell therapy, CAR-T therapy, intentional or unintentional (off-target effects during gene editing) gene mutations induced by viral vectors or CRISPR modifications, organoid studies, organelle quantification, and infectious disease research.

[0007] Furthermore, non-coding RNAs are actively involved in cellular function and specialization. For example, miRNAs are important non-coding, well-conserved post-transcriptional regulators with great potential as diagnostic or prognostic biomarkers. A very recent miRbase release lists 2654 annotated human miRNAs. Large intercellular variability can be observed not only at the mRNA transcript level but also at the (small) non-coding RNA (such as miRNA) transcript level, even when cells are identical at the genetic and morphological levels. Moreover, each individual cell provides its own unique microenvironment. miRNA expression profiles provide insights into cellular state (e.g., human cancer) and cellular mechanisms. It is well known that miRNAs regulate the expression of up to one-third of encoded human genes and influence a wide range of fundamental cellular functions. miRNA profiling in single cells or pools of a small number of cells is always particularly important when examining highly variable expression between cells or when the number of available cells is very limited (e.g., early embryos). Furthermore, in the field of biomarker research, it is particularly important to identify and analyze the expression of specific miRNA molecules in rare cell subsets that cannot be identified in complex cell pools. Analysis of miRNA levels in single-cell / small-cell pools mainly involves five steps: 1) isolation of individual cells, 2) cell lysis and the resulting release of miRNA, which can be stabilized with lysis buffer, 4) polyadenylation and reverse transcription of miRNA, and 5) quantification of miRNA levels (with pre-amplification for small amounts of miRNA if necessary). Rapid and efficient extraction of miRNA from single cells, and its stabilization after cell lysis, are necessary to preserve the cell's innate "miRNome" and reduce output mutations caused by technical issues.

[0008] Single-cell reverse transcription quantitative polymerase chain reaction (scRT-qPCR) and single-cell RNA sequencing (scRNA-seq), along with their technical variations, remain the two main approaches to single-cell RNA analysis. Similarly, qPCR and single-cell DNA sequencing (scDNA-seq) are the two main approaches to single-cell genomic / mitochondrial DNA analysis.

[0009] Currently, quantitative real-time PCR (qPCR) is a widely used method for the quantification of RNA and DNA targets. While qPCR provides rapid and cost-effective analysis of one or more nucleic acid targets of interest, its sensitivity and specificity for target detection depend heavily on the quantity and quality of the template, the PCR conditions, and the presence of PCR inhibitors in the reactants. As outlined in Quan et al. (Quan et al., 2018, MDPI, "dPCR: A Technology Review"), quantitative real-time PCR is based on conventional PCR. In conventional PCR, the target DNA (which may also be reverse-transcribed RNA) is amplified over multiple cycles, and at the end of each 100% efficient PCR cycle, the number of target DNA molecules doubles (exponential amplification). Therefore, theoretically, after n cycles, 2 n It is possible to generate multiple copies. However, in reality, the PCR reagent is depleted at some point, the amplification product self-anneals, and a decrease in amplification efficiency occurs until a plateau is reached and the amplification process saturates. At the end of a conventional PCR reaction, the amplification product can be analyzed using, for example, agarose gel electrophoresis (endpoint measurement), but is not limited to this. The specificity of conventional PCR relies on sequence hybridization. The sensitivity of conventional PCR depends on enzyme-based amplification (Quan et al., 2018, MDPI, "dPCR: A Technology Review").

[0010] As described above, quantitative real-time PCR is based on conventional PCR, but the amount of amplified PCR product is measured after each amplification cycle using fluorescence readings. A typical real-time PCR amplification plot shows a sigmoid-shaped curve (on a linear scale) and includes a baseline phase, followed by a linear phase and then an exponential phase reaching a plateau. The most efficient phase of amplification is represented by the exponential phase, where the amount of amplified PCR product doubles with each cycle (at 100% amplification efficiency). Relative quantification of the target relative to the calibrator is made possible by real-time PCR. The "absolute" amount of the target sequence in a qPCR reaction is measured against a standard curve created from a sample of known quantity or copy number. This method implies that the amplification efficiencies of the standard and the sample are equivalent. Differences in PCR efficiency can significantly affect the accuracy of quantification (Quan et al., 2018, MDPI, "dPCR: A Technology Review").

[0011] For quantification using qPCR, a standard curve is required to calculate the amount of target nucleic acid in the sample. Typically, the standard curve is created by serial dilution by the user. Creating a standard curve is crucial for accurate targeted quantification. Depending on the user's skill and the quality of the equipment used to create the standard curve, the process of diluting cDNA or DNA and creating the DNA standard curve itself can be very cumbersome, time-consuming, and error-prone, which can lead to inaccurate quantification results.

[0012] In contrast to qPCR, single-cell sequencing provides comprehensive RNA or DNA analysis in a high-throughput manner from thousands of individual cells, but its low sequencing coverage and high technical noise can cause it to miss rare targets, especially with small amounts of starting material. Further challenges arise when analyzing and interpreting large datasets obtained using scRNA-seq / scDNA-seq.

[0013] Another approach to quantifying DNA (including reverse-transcribed RNA) utilizes digital polymerase chain reaction (dPCR; also abbreviated as digital PCR, DigitalPCR, or dePCR), which is an improvement over other polymerase chain reaction methods such as qPCR.

[0014] Digital polymerase chain reaction (dPCR) enables the absolute quantification of target nucleic acids present in a sample, mitigating the shortcomings of qPCR. Unlike qPCR, dPCR does not rely on calibration curves for sample quantification, thus avoiding the hidden risks associated with variability in reaction efficiency. dPCR is a method for absolute nucleic acid quantification that relies on the detection of endpoint fluorescence signals and binary events, i.e., the enumeration of the presence or absence of fluorescence in a given compartment. In dPCR, the sample is first divided into many independent PCR sub-reaction compartments, so that each compartment contains either a few target sequences, one target sequence, or no target sequences at all. Several different methods can be used to compartmentalize the sample, including microwell plates, microfluidic nanoplates, capillaries, oil emulsions (e.g., water droplets in oil), and arrays of miniaturized chambers with nucleic acid-binding surfaces. Digital PCR (dPCR) offers several advantages over qPCR and scRNA-seq / scDNA-seq. In dPCR, the sample is distributed into thousands of nanocompartments, with each target molecule moving into a compartment in a random manner. This effectively increases the effective concentration of each target molecule in the PCR reaction, in contrast to conventional PCR where the PCR reaction occurs in the bulk sample. Because endpoint PCR occurs independently in each dPCR compartment, any contaminants or PCR inhibitors that may be present in the sample are tolerated. dPCR eliminates the need for a standard curve because it detects signals from individual compartments and calculates the absolute amount of the target molecule using Poisson statistics. Thanks to these features, dPCR results in the quantification of the target nucleic acid with much higher sensitivity and specificity, regardless of PCR efficiency. Multiplexed analysis of the target of interest allows for focusing on specific targets rather than, for example, the bulk transcriptome or genome. In addition, dPCR overcomes the challenges associated with bioinformatics analysis and data interpretation, enabling researchers to obtain, for example, gene expression profiles or target copy numbers from cells in a rapid, efficient, and cost-effective manner, from isolation to target quantification. Therefore, the use of digital PCR results in quantification with unparalleled sensitivity and specificity for targeted single-cell or population-based nucleic acid expression or copy number analysis. It allows for the quantification of subtle changes in the amount of rare targets at the single-cell level. [Prior art documents] [Non-patent literature]

[0015] [Non-Patent Document 1] Quan et al., 2018, MDPI, "dPCR: A Technology Review" [Overview of the Initiative] [Means for solving the problem]

[0016] Therefore, there is great interest in highly accurate methods for quantifying nucleic acid targets in single cells to achieve highly accurate targeted RNA expression data, or targeted genomic DNA copy numbers, or targeted mitochondrial DNA copy numbers, particularly suitable for analyzing targets in low abundance. Furthermore, there is a need for methods that overcome the shortcomings of currently available methods.

[0017] Summary of the Invention Digital PCR, when combined with highly accurate single-cell sorting or cell isolation equipment, solves two major problems: highly sensitive detection and accuracy of single-cell isolation. This invention provides a highly efficient, high-throughput method for quantifying the amount of nucleic acid targets in clearly defined individual cells or cell populations.

[0018] This invention provides a method for quantifying the amount of a nucleic acid target in clearly defined individual cells or cell populations. It captures the nucleic acid landscape of a single cell or cell population at a specific point in time to analyze the target of interest. Rapid transfer of cells to lysis buffer reduces artifacts because cells have less time to adapt to stressors or environmental changes. Furthermore, the absence of a nucleic acid purification step eliminates DNA and RNA loss. Instead, reverse transcription and digital PCR (or digital PCR alone if RNA targets are not being analyzed) are performed on the nucleic acid of interest or the target nucleic acid. By combining a highly accurate cell sorting / isolation method with rapid cell lysis and plate-based digital PCR as a highly accurate quantification method, even targets present in low abundance can be accurately quantified. The results can be compared with results from other (single) cells.

[0019] Therefore, the present invention relates to a method for quantifying the amount of at least one nucleic acid target, a) a step of providing a specified number of cells, wherein the specified number of cells is at least one cell; and b) lysing at least one cell, thereby releasing RNA and DNA; and c) performing a reverse transcription reaction on the released RNA, thereby generating complementary DNA (cDNA), and further performing digital polymerase chain reaction (dPCR) on at least one reverse transcribed RNA target within the cDNA; and / or performing digital polymerase chain reaction (dPCR) on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) quantifying the amount of at least one nucleic acid target to provide a method comprising the steps.

Mode for Carrying Out the Invention

[0020] Detailed Description of the Invention The present invention is a method for quantifying the amount of at least one nucleic acid target, comprising: a) providing a defined number of cells, wherein the defined number of cells is at least one cell; and b) lysing at least one cell, thereby releasing RNA and DNA; and c) performing a reverse transcription reaction on the released RNA, thereby generating complementary DNA (cDNA), and further performing digital polymerase chain reaction (dPCR) on at least one reverse transcribed RNA target within the cDNA; and / or performing digital polymerase chain reaction (dPCR) on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) quantifying the amount of at least one nucleic acid target [[ID=2�]]to provide a method comprising the steps.

[0021] The present invention is also a method for quantifying the amount of at least one ribonucleic acid (RNA) target, comprising: a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing RNA, wherein the released RNA comprises at least one RNA target; c) A step of removing cellular DNA by performing a DNA digestion step using one or more DNA nucleases as necessary, wherein one or more DNA nucleases are inactivated before the reverse transcription reaction; d) A step of performing a reverse transcription reaction on released RNA containing at least one RNA target, thereby generating complementary DNA (cDNA); e) A step of performing a digital polymerase chain reaction (dPCR) against at least one reverse-transcribed RNA target in cDNA, wherein the dPCR is performed in a plate; f) A step to quantify the amount of at least one RNA target. We also provide methods that include this.

[0022] The present invention also provides a method for quantifying the amount of at least one coding RNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing RNA, wherein the released RNA comprises coding RNA containing at least one coding RNA target; c) A step of removing cellular DNA by performing a DNA digestion step using one or more DNA nucleases as necessary, wherein one or more DNA nucleases are inactivated before the reverse transcription reaction; d) A step of performing a reverse transcription reaction on released RNA containing a coding RNA containing at least one coding RNA target, thereby generating complementary DNA (cDNA); e) A step of performing a digital polymerase chain reaction (dPCR) against at least one reverse-transcribed coding RNA target in cDNA, wherein the dPCR is performed in a plate; f) A step to quantify the amount of at least one coding RNA target. We also provide methods that include this.

[0023] The present invention also provides a method for quantifying the amount of at least one non-coding RNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing RNA, wherein the released RNA comprises a non-coding RNA comprising at least one non-coding RNA target; c) A step of removing cellular DNA by performing a DNA digestion step using one or more DNA nucleases as necessary, wherein one or more DNA nucleases are inactivated before the reverse transcription reaction; d) A step of performing a reverse transcription reaction on released RNA containing a non-coding RNA that includes at least one non-coding RNA target, thereby generating complementary DNA (cDNA); e) A step of performing a digital polymerase chain reaction (dPCR) against at least one reverse-transcribed non-coding RNA target in cDNA, wherein the dPCR is performed in a plate; f) A step of quantifying the amount of at least one noncoding RNA target. We also provide methods that include this.

[0024] The present invention also provides a method for quantifying the amount of at least one small non-coding RNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing RNA, wherein the released RNA comprises a small non-coding RNA comprising at least one small non-coding RNA target; c) A step of removing cellular DNA by performing a DNA digestion step using one or more DNA nucleases as necessary, wherein one or more DNA nucleases are inactivated before the reverse transcription reaction; d) A step of performing a reverse transcription reaction on released RNA containing a small non-coding RNA that includes at least one small non-coding RNA target, thereby generating complementary DNA (cDNA); e) A step of performing a digital polymerase chain reaction (dPCR) against at least one reverse-transcribed small non-coding RNA target in cDNA, wherein the dPCR is performed in a plate; f) A step of quantifying the amount of at least one small noncoding RNA target. We also provide methods that include this.

[0025] The present invention also provides a method for quantifying the amount of at least one deoxyribonucleic acid (DNA) target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing DNA; c) A step of performing a digital polymerase chain reaction (dPCR) on at least one DNA target in the released DNA, wherein the dPCR is performed in a plate; d) A step of quantifying the amount of at least one DNA target. We also provide methods that include this.

[0026] The present invention also provides a method for quantifying the amount of at least one genomic DNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing genomic DNA; c) A step of performing a digital polymerase chain reaction (dPCR) on at least one genomic DNA target within the released genomic DNA, wherein the dPCR is performed in a plate; d) A step to quantify the copy number of at least one genomic DNA target. We also provide methods that include this.

[0027] The present invention also provides a method for quantifying the amount of at least one mitochondrial DNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing mitochondrial DNA; c) A step of performing a digital polymerase chain reaction (dPCR) on at least one mitochondrial DNA target within the released mitochondrial DNA, wherein the dPCR is performed in a plate; d) A step to quantify the amount of at least one mitochondrial DNA target. We also provide methods that include this.

[0028] The present invention also provides a method for quantifying the amount of at least one genomic DNA target and at least one mitochondrial DNA target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing genomic DNA and mitochondrial DNA; c) A step of performing a digital polymerase chain reaction (dPCR) on at least one genomic DNA target in the released genomic DNA and at least one mitochondrial DNA target in the released mitochondrial DNA, wherein the dPCR is performed in a plate; d) A step to quantify the amount of at least one genomic DNA target and at least one mitochondrial DNA target. We also provide methods that include this.

[0029] dPCR is performed in a plate. Furthermore, in one embodiment of all the methods of the present invention, one or both of steps i) lysis and ii) reverse transcription (if applicable) are performed independently of each other in one or more different plates or one or more different reaction vessels (referred herein to as Falcon tubes or Eppendorf tubes or similar vessels known in the art).

[0030] In other embodiments of all the methods of the present invention, lysis, reverse transcription (if applicable), and dPCR are performed in the same plate.

[0031] The present invention also provides a method for quantifying the amount of at least one nucleic acid target, a) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell, thereby releasing RNA and DNA; c) a step of performing a reverse transcription reaction on the released RNA to generate complementary DNA (cDNA), and further performing a digital polymerase chain reaction (dPCR) on at least one reverse-transcribed RNA target in the cDNA, wherein the dPCR is performed in a plate; and / or A step of performing a digital polymerase chain reaction (dPCR) against at least one DNA target in the released DNA, wherein the dPCR is performed in a plate; and d) A step of quantifying the amount of at least one nucleic acid target. We also provide methods that include this.

[0032] In one embodiment, steps b) and c) above are performed in the same plate.

[0033] In one embodiment, the reverse transcription reaction in step c) above is also carried out in a plate, preferably in the same plate in which the subsequent dPCR is performed.

[0034] In an alternative embodiment, the cells according to the present invention are not placed in a lysis buffer but are instead loaded onto a plate and lysed by heat (thermal lysis). Therefore, when using a nanoplate, a specified number of cells can be loaded into the wells. Subsequently, the intact cells are distributed into compartments and then lysed by thermal lysis. The released nucleic acids can then be subjected to reverse transcription and dPCR, if necessary.

[0035] Therefore, the present invention also provides a method for quantifying the amount of at least one nucleic acid target, a) A step of providing a specified number of cells onto a plate, wherein the specified number of cells is at least one cell; b) A step of lysing at least one cell by thermal lysis, thereby releasing RNA and / or DNA; c) a step of performing a reverse transcription reaction on the released RNA to generate complementary DNA (cDNA), and further performing a digital polymerase chain reaction (dPCR) on at least one reverse-transcribed RNA target in the cDNA, wherein the dPCR is performed in a plate; and / or A step of performing a digital polymerase chain reaction (dPCR) against at least one DNA target in the released DNA, wherein the dPCR is performed in a plate; and d) A step of quantifying the amount of at least one nucleic acid target. We also provide methods that include this.

[0036] The present invention also provides an in vitro method for assessing the risk of a subject developing cancer or disease, wherein the cancer or disease is associated with the disordered expression of at least one coding RNA target, or the disordered expression of at least one non-coding RNA target, or with at least one genomic or mitochondrial structural variability, preferably copy number variation (CNV). The quantitative method according to the present invention can be used to determine the amount of at least one nucleic acid target of interest. In certain embodiments, this amount can serve as a prognostic marker known in the art, thus allowing for the assessment of the subject's risk of developing cancer or disease.

[0037] The present invention also provides an in vitro method for diagnosing a subject having cancer or disease, wherein the cancer or disease is associated with the disordered expression of at least one coding RNA target, or the disordered expression of at least one non-coding RNA target, or with structural variability, preferably copy number variation (CNV), of at least one genome or mitochondria. The quantitative method according to the present invention can be used to determine the amount of at least one nucleic acid target of interest. In certain embodiments, this amount is a biomarker for cancer or a specific disease, and since it is known in the art, the subject can be diagnosed with cancer or a specific disease.

[0038] The present invention also provides an in vitro method for monitoring the progression of cancer or disease in a subject, wherein the cancer or disease is associated with the disordered expression of at least one coding RNA target, or the disordered expression of at least one non-coding RNA target, or with at least one genomic or mitochondrial structural variability, preferably copy number variation (CNV). Using the quantitative method according to the present invention, the amount of at least one nucleic acid target of interest can be determined. In certain embodiments, this amount can serve as a prognostic marker known in the art, thus allowing for monitoring of the progression of cancer or disease in a subject.

[0039] The present invention also provides an in vitro method for monitoring a subject's response to treatment for cancer or disease, wherein the cancer or disease is associated with the disordered expression of at least one coding RNA target, or the disordered expression of at least one non-coding RNA target, or at least one genomic or mitochondrial structural variability, preferably copy number variation (CNV). Using the quantitative method according to the present invention, the amount of at least one nucleic acid target of interest can be determined. In certain embodiments, this amount can serve as a prognostic marker known in the art, thus allowing monitoring of the subject's response to treatment for cancer or disease.

[0040] In this regard, the following literature discloses examples of dysregulated RNA or DNA targets associated with cancer or other diseases. Farazi et al. ("miRNAs in human cancer", 2011, Journal of Pathology, DOI: 10.1002 / path.2806) discloses several miRNAs associated with cancer, as well as the abundance of some miRNAs in various cancers. Similarly, Condrat et al. ("miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis", 2020, Cells, DOI: 10.3390 / cells9020276) provides an overview of miRNAs and their association with various cancers and other diseases. This review also discusses the topic of miRNAs as biomarkers in disease, including their roles in diagnosis and prognosis prediction. Henriksen et al. ("Single Cell Analysis Identifies the miRNA Expression Profile of a Subpopulation of Muscle Precursor Cells Unique to Humans With Type 2 Diabetes", 2018, Front. Physiol., DOI: 10.3389 / fphys.2018.00883) have demonstrated that the miRNA expression profile of a subpopulation of muscle progenitor cells is associated with type 2 diabetes in humans.Rai et al. ("Analysis of mitochondrial DNA copy number variation in blood and tissue samples of metastatic breast cancer patients (A pilot study)", 2021, Biochemistry and Biophysics Reports, DOI: 10.1016 / j.bbrep.2021.100931) revealed different mitochondrial DNA copy numbers in breast cancer tissue compared to non-tumor breast tissue. Fazzini et al. ("Association of mitochondrial DNA copy number with metabolic syndrome and type 2 diabetes in 14,176 individuals", 2021, J Intern Med, DOI: 10.1111 / joim.13242) were able to demonstrate an association between mitochondrial DNA copy number and metabolic syndrome and type 2 diabetes. Reznik et al. ("Mitochondrial DNA copy number variation across human cancers", 2016, eLIFE, DOI: 10.7554 / eLife.10769.001) discloses the existence of mitochondrial DNA copy number variations across various human cancers. Shlien and Malkin ("Copy number variations and cancer", 2009, Genome Medicine, DOI: 10.1186 / gm62) outline the role of DNA copy number variations in cancer. Wong et al. ("A Comprehensive Analysis of Common Copy-Number Variations in the Human Genome", 2006, Am J Hum Genet., DOI: 10.1086 / 510560) discloses the association between certain CNVs and several diseases, including sense-related disorders and cancer.Andhale and Shrivastava ("Huntington's Disease: A Clinical Review", 2022, Cureus, DOI: 10.7759 / cureus.28484) is a review of Huntington's disease, an autosomal dominant inherited condition caused by a CAG trinucleotide repeat extension in the huntingtin gene on chromosome 4. Ding et al. ("Evaluating the molecule-based prediction of clinical drug responses in cancer", 2016, Bioinformatics, DOI: 10.1093 / bioinformatics / btw344) have shown that the expression levels of specific mRNAs or miRNAs in certain cancer types may serve as predictors of drug response.

[0041] The present invention also provides a kit for carrying out the method according to the present invention. Such a kit may include a suitable buffer, primers, probes, and other components required to carry out the method according to the present invention.

[0042] As used herein, “genomic DNA target” generally refers to a region on genomic DNA. In a preferred embodiment, “genomic DNA target” refers to a region on genomic DNA that harbors or is suspected of harboring copy number variations (CNVs). CNVs are structural changes in the genome (such as deletions, insertions, replications, translocations, and inversions) that result in an increase or decrease in the copy number of a region, ranging from a few hundred base pairs to entire chromosomes. CNVs can be hereditary or the result of de novo somatic changes. CNVs account for up to 10–20% of variability in the genome and are the source of natural genetic diversity and biological dysfunction in humans. CNVs often result in disruption of gene function, gene load imbalance, chromosome end shortening, and positional effects, which are associated with complex diseases and traits, including but not limited to cancer, obesity, aging, and neurodegenerative and autoimmune diseases. Therefore, quantitative analysis of CNVs at disease-associated loci provides insights into the molecular mechanisms of disease and offers the potential for the discovery of novel biomarkers.

[0043] Therefore, in certain embodiments, copy number variations in regions of genomic DNA may be associated with an increased risk of disease, an increased risk of disease progression, an increased risk of cancer development, an increased risk of cancer progression, or an increased risk of obesity.

[0044] As used herein, “mitochondrial DNA target” generally refers to a region on mitochondrial DNA. In preferred embodiments, “mitochondrial DNA target” refers to a region on mitochondrial DNA that harbors or is suspected of harboring copy number variations (CNVs). Similar to genomic CNVs, changes in mitochondrial DNA copy number (mtDNA CN) in blood or tissue are associated with disease, which is not surprising given the crucial role mitochondria play in cellular homeostasis. In contrast to the constant copy number of genomic DNA (a healthy diploid state), significant intercellular variability can be observed at the mitochondrial DNA (mtDNA) level, even when cells are identical in terms of genetic identity and morphology. mtDNA copy number varies and changes between individuals, between different tissues and cells, and even between mitochondria within the same cell. Individual cells can harbor hundreds to hundreds of thousands of copies of mtDNA. However, large changes in mtDNA copy number can lead to mitochondrial dysfunction and disease development. Therefore, capturing mtDNA copy number heterogeneity, similar to genomic CNVs, is important for disease and drug development research and can be used as an alternative for screening metabolic diseases, cancer, prenatal testing, neurodegenerative diseases, and age-related disorders.

[0045] As used herein, “RNA target” refers to any type or subset of RNA to be analyzed / quantified according to the method of the present invention. Thus, RNA targets include, in particular, coding and non-coding RNA targets. The RNA targets must be reverse transcribed into cDNA prior to analysis, and subsequently, dPCR is performed on at least one reverse-transcribed RNA target within the cDNA.

[0046] As used herein, "coding RNA target" refers to an RNA target that codes for a peptide / protein. Therefore, this term refers to, but is not limited to, messenger RNA (mRNA), such as β-actin (ACTB) mRNA.

[0047] Regarding coding RNA targets, approximately 360,000 mRNA molecules exist within a single mammalian cell. Some mRNAs constitute 3% of the mRNA pool, while others account for less than 0.1%. These rare or low-copy mRNAs may have as few as 0 or 1 copy per cell.

[0048] As used herein, "non-coding RNA targets" refer to RNA targets that do not code for peptides / proteins. However, they may have specialized functions. According to Li and Chen ("Small and Long Non-Coding RNAs: Novel Targets in Perspective Cancer Therapy", 2015, Curr Genomics), these functions include, but are not limited to, the regulation of transcription and translation, or function in protein scaffolds. Non-coding RNAs include long non-coding RNAs (≥200 nt) and small non-coding RNAs (<200 nt). Examples of long non-coding RNA targets include long intergenetic non-coding RNAs (lincRNAs), natural antisense transcripts (NATs), transcribed superconserved regions (T-UCRs), and non-coding pseudogenes. Examples of small non-coding RNA targets include microRNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs).

[0049] Unless otherwise specified, “DNA target” as used herein refers to genomic DNA targets and mitochondrial DNA targets. Depending on the context, this may refer to both or one of them. For example, if the sentence “dPCR is performed against at least one DNA target,” this means that if dPCR is performed against one DNA target, this target may be either a genomic DNA target or a mitochondrial DNA target. However, if dPCR is performed against two DNA targets in this example, each of these two DNA targets may be selected independently from genomic DNA targets and mitochondrial DNA targets. The same principle applies to more than two DNA targets. The same general principle also applies to “RNA targets.” Where “nucleic acid target” (or simply “target”) is used herein, this means that one of each of these nucleic acid targets may be selected independently from the various DNA targets and RNA targets described herein.

[0050] Therefore, in one embodiment, each of at least one nucleic acid target is independently selected from coding RNA targets; non-coding RNA targets; genomic DNA targets; and mitochondrial DNA targets. Preferably, the coding RNA target is mRNA; the non-coding RNA target is miRNA; the genomic DNA target is a region on genomic DNA that harbors or is suspected of harboring copy number variations (CNVs); and the mitochondrial DNA target is a region on mitochondrial DNA that harbors or is suspected of harboring CNVs.

[0051] In one embodiment, each of at least one nucleic acid target is independently selected from mRNA; miRNA; regions on genomic DNA that harbor or are suspected of harboring copy number variations (CNVs); and regions on mitochondrial DNA that harbor or are suspected of harboring CNVs.

[0052] In one embodiment, the structural variability of the genome is copy number variation.

[0053] In one embodiment, the structural variability of mitochondria is copy number variation.

[0054] In one embodiment, the small non-coding RNA is a miRNA.

[0055] As used herein, the term “at least one” means one, two, three, or more. For example, “at least one target” means each containing one, two, three, or more targets. Similarly, as used herein, “at least two” means two, three, four, or more. For example, “at least two targets” means each containing two, three, four, or more targets.

[0056] As used herein, the term "accuracy" refers to a measure of truthfulness. Accuracy is how close a given set of measurements are to their true values. This is a description solely of systematic error, which is a measure of statistical bias on a given measure of central tendency. Low accuracy results in a difference between the result and the true values.

[0057] "Accuracy" should not be confused with "precision," which defines how close the measured values ​​are to each other. In other words, precision is a description of random error and a measure of statistical variability.

[0058] According to the method of the present invention, the source sample from which a specified number of cells are provided includes eukaryotic cells, human cells, animal cells, plant cells, bacterial cells, archaeal cells, oomycete cells, viral cells, and / or fungal cells.

[0059] Preferably, the source sample from which a specified number of cells are provided is a eukaryotic sample. Most preferably, the source sample from which a specified number of cells are provided is a human sample.

[0060] In one embodiment, the source sample from which a specified number of cells are provided may be either a male or female sample. Alternatively, the sample may contain a mixture of male and female cells. Thus, according to another embodiment, the sample may contain one or more additional cells derived from different individuals.

[0061] Therefore, in one embodiment, at least one cell is derived from a eukaryotic sample, preferably a human sample. In one embodiment, the human sample is derived from a subject who has or is suspected of having cancer. In another embodiment, the human sample is derived from a subject who has or is suspected of having a disease.

[0062] In one embodiment, the source sample from which a specified number of cells are provided comes from a subject that has or is suspected of having cancer. In another embodiment, the source sample from which a specified number of cells are provided comes from a subject that has or is suspected of having a disease.

[0063] In certain embodiments, cancer or disease is associated with the disordered expression of at least one coding RNA target. The amount of at least one coding RNA target can be quantified by the method according to the present invention.

[0064] In certain other embodiments, cancer or disease is associated with the disordered expression of at least one non-coding RNA target, preferably at least one small non-coding RNA target. The amount of at least one non-coding RNA target can be quantified by the method according to the present invention.

[0065] In some other specific embodiments, cancer or disease is associated with structural variability, preferably copy number variation (CNV), of at least one genome or mitochondria. The amount of structural variability of at least one genome or mitochondria can be quantified by the method according to the present invention, i.e., by quantifying the amount of at least one genomic DNA target and / or at least one mitochondrial DNA target.

[0066] (Single-cell) CNV analysis can be applied to gene therapy and cell therapy, CAR-T therapy, intentional gene mutations induced by viral vectors or CRISPR modifications, organoid studies, organelle quantification, and infectious disease research.

[0067] In one embodiment, the source sample from which a specified number of cells are provided may originate from one of the following sample or tissue types, including whole blood, blood fraction, plasma, serum, tumor cells, body fluids, oral specimens, oral fluid, saliva, sputum, swabs, urine, human biological tissue, clothing specimens containing biological material, vaginal swabs, sperm, skin or wound swabs, or other specimens containing biological material or other parts of the human body that can be used for nucleic acid isolation. As used herein, the terms “oral fluid” and “body fluid” refer to liquids discharged or secreted from the oral cavity and the body, respectively, from which nucleic acids can be isolated. Non-limiting examples of oral fluid and body fluids include saliva, sputum, swabs, and urine.

[0068] In one embodiment, the source sample from which a specified number of cells are provided contains cells or cell types or cell lines, or a mixture of different, i.e., at least two, cells or cell types or cell lines, in a (cell) suspension. In one embodiment, the cells or cell types or cell lines include genetically modified cells, cell types or cell lines. In one embodiment, the cells or cell types or cell lines include genetically unmodified cells, cell types or cell lines. In one embodiment, the cells or cell types or cell lines include genetically modified cells, cell types or cell lines and unmodified cells, cell types or cell lines.

[0069] If necessary, the preparation of cells from a sample prior to the step of providing a specified number of cells can be carried out by appropriate and applicable methods known in the art, which include, for example, trypsin treatment of cell cultures, if applicable.

[0070] A precisely defined number of cells can be obtained by various methods. In one embodiment, two, three, four, five, or more cells are isolated or sorted and thus provided. In a preferred embodiment, a single cell is provided.

[0071] In a preferred embodiment of the method of the present invention, a specified number of cells are provided by a method comprising or selected from the group comprising fluorescence-activated cell sorting, micromanipulation, microfluidics, immunopanning, magnetoactivated cell sorting, and laser microdissection. In a more preferred embodiment of the method of the present invention, the specified number of cells is at least one cell, and this at least one cell is provided by a method comprising or selected from the group comprising fluorescence-activated cell sorting, micromanipulation, microfluidics, immunopanning, magnetoactivated cell sorting, and laser microdissection.

[0072] Fluorescence-activated cell sorting (FACS) is a commonly used technique to isolate specific cells of interest, either as single cells or as a pool of cells of interest. The cells of interest are, though not always, usually fluorescently labeled and pass through a flow cytometer. A laser excites fluorophores. The emitted light is detected, and the cells of interest can be separated from other cells for use in subsequent steps. Cells can also be isolated based on their size and / or their light scattering profile. It is possible.

[0073] Microscopy can be performed manually, i.e., by a person skilled in the art picking up individual cells using a microscope and micropipette, or mechanically, i.e., by using, for example, a robot that picks up individual cells based on visual input, etc.

[0074] Microfluidics allows for the isolation of target cells using small amounts of fluid and sample. Cells can be isolated based on labeling, or, but not limited to, cell-specific characteristics such as size, shape, density, deformability, electrical polarizability / impedance, and other hydrodynamic properties. Individual cells can be separated, for example, via traps, valves, or droplets, but other methods for separating them that are known in the art are also included in the scope of the methods of this invention.

[0075] Immunopanning is used to isolate cells based on their binding to immobilized antibodies. Specifically, these antibodies bind to surface antigens on the target cells. Unbound cells can be washed away, and the antibody-bound cells of the desired type can be recovered.

[0076] In magnetically activated cell sorting (MACS), target cells bind to antibodies, which then bind to magnetic beads. Specifically, these antibodies target antigens present on the target cells. When a magnetic field is applied, the magnetic beads are attracted, allowing unbound cells to pass between them. Subsequently, the target cells bound to the beads are eluted and can be used in subsequent steps. Alternatively, the target cells may not bind to the beads, and unwanted cells may become bound by the beads. In this case, the passing cells need to be collected, but the cells bound to the beads can be discarded.

[0077] Laser microdissection (LMD), also known as laser capture microdissection (LCM), can be used to isolate desired cells from a tissue sample by applying a laser beam to cut a desired region that has been previously characterized using a microscope. This method can be used to obtain single or multiple cells.

[0078] In addition to the above, the scope also includes similar cell isolation methods known in the art that enable the isolation of a precise number of cells, such as a single cell, two cells, three cells, four cells, five cells, and more than five cells. One example is the cellenONE® X1 technique, which is based on an image (bright-field and / or 4-channel fluorescence)-based method for selecting and isolating single cells. A sample of only 1 μl containing just a few cells can be processed using this technique, and the resulting droplets can have a volume of 150–600 pL.

[0079] Preferably, the diameter, extension parameters, and / or any targetable structure, such as, but not limited to, size, shape, particle size, protein or peptide, and / or nucleic acid, are known within or on one or more cells of interest, or their information can be obtained during the process of the present invention. These parameters or structures can be used for the isolation of one or more cells of interest.

[0080] In a preferred embodiment, at least one of the provided cells is isolated directly into the lysis buffer. This preserves the cell's RNA landscape as well as possible. Cells adapt their transcriptome very rapidly to changes in their environment or stressors. Furthermore, ribonucleases present in cells or the environment can rapidly degrade RNA. Therefore, RNA is relatively unstable, and the faster the cells of interest are lysed, the less artifact or variability there will be in the RNA landscape, which is a critical requirement for single-cell RNA analysis. In principle, all types of RNA present in a cell can be subjected to analysis within the scope of each method of the present invention, for example, mRNA and / or miRNA can be analyzed, but are not limited to these. In one embodiment, cell lysis and all subsequent steps of the method of the present invention, namely reverse transcription (if analyzing RNA) and dPCR, are performed in the same plate. The advantage of using one plate for all steps from lysis to dPCR is that no material is lost through the pipetting and purification steps, and it is time-efficient because the procedure requires fewer pipetting steps. In addition, the risk of contamination during handling is minimized. Furthermore, the cells to be analyzed are intact before being loaded onto the plate, and therefore, when loaded onto the nanoplate, their nucleic acid contents are distributed across the compartments of the nanoplate.

[0081] In one embodiment, both RNA and DNA are released by lysis of at least one cell. In this embodiment, DNA targets and / or RNA targets can be analyzed.

[0082] "Lysing at least one cell and thereby releasing RNA" means, as used herein, that at least RNA is released. The release of further types of nucleic acids, such as genomic DNA or mitochondrial DNA, is optional if they do not contain the target of interest.

[0083] "Lysing at least one cell and thereby releasing DNA" means, as used herein, that at least genomic DNA and / or mitochondrial DNA are released. The release of further types of nucleic acids, e.g., RNA, may be done as needed if they do not contain the target of interest.

[0084] "Lysing at least one cell to release mitochondrial DNA" means, as used herein, that at least mitochondrial DNA is released. The release of further types of nucleic acids, such as genomic DNA or RNA, may be done as needed if they do not contain the target of interest.

[0085] If direct isolation into a lysis buffer is not possible, for example, depending on requirements that depend on a specific isolation or sorting device, or the requirements of the kit used, the cells may be isolated in phosphate-buffered saline or a different buffer, or in a suitable culture medium, and then rapidly lysed. In one preferred embodiment, the lysed cells are used directly for subsequent steps, for example, for reverse transcription and dPCR reactions, or for dPCR reactions if RNA is not analyzed. However, the method according to the present invention also allows for the storage of the lysate before the subsequent steps of the method according to the present invention are performed. Thus, in another embodiment, the lysate is stored, for example, at approximately 4°C, or -20°C, or -80°C, until it is used in a subsequent step of the method according to the present invention.

[0086] In a preferred embodiment of the method according to the present invention, the lysis buffer contains a component that stabilizes RNA and / or DNA. In an alternative embodiment, the component that stabilizes RNA and / or DNA may be added to the solution containing the cells to be sorted or isolated, or to the final solution of cells used immediately before lysis. In another alternative embodiment, the component that stabilizes RNA and / or DNA may be added immediately after cell lysis.

[0087] Similar to the rapid lysis of cells described above, RNA stabilization after or during cell lysis helps preserve the cell's native RNA landscape. Currently, commercially available kits are available for i) the preparation of RNA lysates directly from cells using one or more buffers, ii) the stabilization of cellular RNA, and, if necessary, iii) the removal of contaminating DNA. These kits eliminate the need for RNA purification steps and accelerate and streamline RNA sample preparation from cells in a high-throughput and reproducible manner. They also have a positive impact on the accuracy of the present invention. Thus, the method according to the present invention for the analysis of RNA targets does not require a further RNA extraction step after cell lysis. Instead, the lysate or its fraction can be used directly in subsequent steps. Because no additional RNA purification step is required, this further accelerates the procedure and avoids the loss of RNA, especially low-abundance RNA, which might otherwise be lost during the additional purification step.

[0088] Similarly, commercially available kits are available for i) the direct preparation of lysates from cells using one or more buffers, and, if necessary, ii) the stabilization of cellular RNA and, if necessary, DNA. These kits eliminate the need for RNA and / or DNA purification steps, accelerating and streamlining sample preparation from cells in a high-throughput and reproducible manner. They also have a positive impact on the accuracy of the present invention. Thus, the method according to the present invention does not require an additional RNA and / or DNA extraction step after cell lysis. Instead, the lysate or its fraction can be used directly in subsequent steps. Because no additional RNA and / or DNA purification step is required, this further accelerates the procedure and avoids the loss of nucleic acids, particularly low-abundance nucleic acid targets, that might otherwise be lost during the additional purification step.

[0089] In a preferred embodiment of the method according to the present invention for the analysis of DNA targets, the lysis buffer does not contain DNA nucleases, such as, but not limited to, DNase I. However, if only RNA targets are being analyzed, one or more DNA nucleases may be added after lysis and before the reverse transcription reaction. In fact, when RNA targets rather than DNA targets are being analyzed, the addition of one or more DNA nucleases is preferred to eliminate the risk of detecting genomic DNA instead of complementary DNA (cDNA) in a subsequent step of the method according to the present invention. Standard DNA nuclease concentrations known in the art can be used. Subsequently, one or more DNA nucleases are inactivated before the reverse transcription step. Inactivation can be carried out by using a DNA nuclease inhibitor, or by a DNA nuclease-resistant aptamer that targets the DNA nuclease, or by chelating the ions required for DNA nuclease activity, or by RNA purification, or by DNA nuclease precipitation. Preferably, DNA nuclease inactivation is carried out by thermal inactivation, for example, by incubation at 75°C for 5 minutes, but not limited to.

[0090] Therefore, in one embodiment, the DNA digestion step using one or more DNA nucleases to remove cellular DNA is performed after lysis and before the reverse transcription reaction, and the one or more DNA nucleases are inactivated before the reverse transcription reaction takes place.

[0091] The lysis buffer may also contain a proteinase, such as proteinase K. If proteinase K is present in the lysis, the sample is thermally inactivated before the subsequent step. Thermal inactivation protocols for proteinase K are known in the art, and one non-limiting example is incubation at 75°C for 5-10 minutes.

[0092] Therefore, in one embodiment, the dissolved material is thermally deactivated before the subsequent steps.

[0093] In another embodiment, the dissolved material is not thermally deactivated before the subsequent step.

[0094] 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1%, or 100%-1%, or 100%-10%, or 100%-20%, or 90%-1%, or 90%-10%, or 90%-20%, or 100%-80%, or 20%-1%, or 70%-30% of the lysate is used in subsequent steps according to the method of the present invention. The amount of lysate used may vary depending on the subsequent steps, the amount of starting material, and the experimental conditions. For example, a lysate containing a large number of cells may require, or may benefit from, the use of a smaller lysate input for downstream steps.

[0095] The direct use of lysates in subsequent steps according to the method of the present invention, without prior purification of DNA and / or RNA, has the advantage of no loss of nucleic acids during purification. As a result, even targets present in very low abundance (e.g., RNA targets present in amounts as low as one copy per cell) can be accurately detected and quantified in absolute terms using the method according to the present invention.

[0096] In one embodiment, dissolution is carried out in the reaction vessel. In an alternative embodiment, dissolution is carried out in a plate different from the plate on which the reverse transcription reaction is carried out. Alternatively or additionally, dissolution is carried out in a plate different from the plate on which dPCR is carried out. In another embodiment, dissolution is carried out in the same plate on which the reverse transcription reaction and dPCR are carried out, if any.

[0097] In one embodiment, for dPCR, or for reverse transcription in which dPCR is directly followed, the dPCR reaction mixture or RT-dPCR reaction mixture containing the lysate or fraction thereof is each distributed into a plurality of compartments before the (reverse transcription and) amplification step, and the (reverse transcription and) dPCR is performed in each of the plurality of compartments.

[0098] In a preferred embodiment of the method according to the present invention for the analysis of RNA targets, the reverse transcription reaction is performed directly on the released RNA of the lysate or on a fraction of the released RNA of the lysate (e.g., on the target of interest). Specifically, the reverse transcription reaction is performed on released RNA containing at least one RNA target. In one embodiment, random primers, target-specific primers, or oligo(dT) primers can be used for reverse transcription. Reverse transcription produces complementary DNA (cDNA). In one embodiment, the reverse transcription reaction is performed in a reaction vessel. In an alternative embodiment, the reverse transcription reaction is performed in a plate different from the plate on which dPCR is subsequently performed. In another embodiment, the reverse transcription reaction is performed in the same plate on which dPCR is subsequently performed. The cDNA or a fraction thereof is used as a template for the subsequent dPCR reaction.

[0099] In one preferred embodiment, the cDNA is used directly for dPCR. However, since cDNA is relatively stable compared to RNA, the method according to the present invention also allows for the storage of cDNA before subsequent steps of the method according to the present invention are performed. Thus, in another embodiment, the cDNA is stored until the dPCR reaction is performed on the cDNA or a fraction thereof. The storage of cDNA can be carried out at approximately 4°C, or -20°C, or -80°C.

[0100] 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, 1%, or 100%-1%, or 100%-10%, or 100%-20%, or 90%-1%, or 90%-10%, or 90%-20%, or 100%-80%, or 20%-1%, or 70%-30% of the cDNA is used in subsequent steps according to the method of the present invention. The amount of cDNA used may vary depending on the subsequent steps, the amount of starting material, and the experimental conditions. For example, cDNA from a large number of cells may require, or may benefit from, the use of less cDNA input for downstream steps.

[0101] In one embodiment, dPCR is performed on at least one reverse-transcribed RNA target within the cDNA. In this embodiment, dPCR can be performed simultaneously on one, two, three, four, or five reverse-transcribed RNA targets within the cDNA. Each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets. Alternatively, each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets.

[0102] In another embodiment, dPCR is performed on at least one DNA target in the released DNA. In this embodiment, dPCR can be performed simultaneously on one, two, three, four, or five DNA targets in the released DNA. Each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets. Alternatively, each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets.

[0103] In yet another embodiment, dPCR is performed on at least one reverse-transcribed RNA target in the cDNA and at least one DNA target in the released DNA. In this embodiment, dPCR can be performed simultaneously on one, two, three, or four DNA targets in the released DNA, while the other targets are reverse-transcribed RNA targets in the cDNA, and dPCR can be performed on a total of up to five nucleic acid targets. Each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets. Alternatively, each of the at least one target may be independently selected from a group comprising multiple low-copy targets, medium-copy targets, and high-copy targets.

[0104] In one embodiment, dPCR is performed simultaneously on one, two, three, four, or five targets within the cDNA and / or released DNA.

[0105] In one embodiment, each of the at least one target by the method of the present invention is independently selected from the group comprising one low-copy target, an intermediate-copy target, and a high-copy target; or, in the case of more than one target, from the group comprising multiple low-copy targets, intermediate-copy targets, and high-copy targets.

[0106] In one embodiment, each of the at least one target by the method of the present invention is independently selected from the group consisting of one low-copy target, an intermediate-copy target, and a high-copy target; or, in the case of more than one target, from the group consisting of multiple low-copy targets, intermediate-copy targets, and high-copy targets.

[0107] The RT-dPCR reaction mixture (for RNA-targeted analysis, or RNA and DNA-targeted analysis), or the dPCR reaction mixture (for DNA-targeted analysis), contains lysed cells or cells of interest, as well as the necessary enzymes, buffer, and dNTPs.

[0108] As used herein, the term "dNTP" refers to deoxyribonucleoside triphosphate. Non-limiting examples of such dNTPs are dATP, dGTP, dCTP, dTTP, and dUTP, which may also exist in the form of labeled derivatives, including, for example, fluorescently labeled, radiolabeled, or biotin-labeled. dNTPs having modified bases are also included in the scope, these bases being, for example, hypoxanthine, xanthine, 7-methylguanine, inosine, xanthoinosine, 7-methylguanosine, 5,6-dihydrouracil, 5-methylcytosine, pseudouridine, dihydrouridine, or 5-methylcytidine. Furthermore, ddNTPs of the above molecules are also included in the scope of the present invention.

[0109] In one embodiment, the method according to the present invention is a high-throughput method.

[0110] Detection of amplification products may include direct or indirect fluorescence and / or chemical labeling of the amplicon by a composition comprising one or more of the following, and / or polynucleotide-based and / or peptide or protein-based labeling: probe and / or guide molecules and / or proteins and / or peptides and / or nucleic acids and / or derivatives of the listed components. In one embodiment, the probe and / or amplification product may be labeled with one or more fluorophores, quenchers, barcodes, indices, peptides, proteins, radioactive tags, biotin tags and / or chemical portions associated therewith. In another embodiment, or further, the probe and / or amplification product may include modifications such as base modifications, sugar modifications and / or skeletal modifications. The probe may be an RNA probe or a DNA probe. Non-limiting examples of probes include Taqman probes, Scorpion probes, Molecular-Beacon probes and / or fluorescence resonance energy transfer probes.

[0111] Alternatively, instead of a probe, an intercalation dye or other known DNA labeling or detection agent may be used.

[0112] Preferably, quantification is based on the detection of at least one amplified nucleic acid target by using at least one probe for each target being analyzed, wherein at least one probe specifically binds to each of the amplified products for each nucleic acid target. For this reason, different probes are required for different targets. Preferably, at least one probe is fluorescently labeled.

[0113] In one embodiment, at least one probe is at least one TaqMan probe.

[0114] In one embodiment, quantification is based on the detection of at least one amplified reverse-transcribed RNA target and / or at least one amplified DNA target by using at least one fluorescently labeled probe for each target to be analyzed, wherein at least one probe specifically binds to each of the amplification products for each target.

[0115] Fluorescence is typically detected for all compartments per well using a high-power detection module with a fluorescence filter. Several fluorescent dyes can be used, as long as the corresponding fluorescence channels can be separated from each other. Examples of dyes include, but are not limited to, HEX, VIC, JOE, FAM, EvaGreen®, TAMRA, ATTO 550, ROX, Texas Red, Cy5, and Cy5.5.

[0116] In addition to cell isolation and lysate production methods, the techniques used for nucleic acid quantification strongly influence the accuracy and robustness of the results.

[0117] Following PCR, the concentration of the target nucleic acid is quantified with statistically defined accuracy using Poisson statistics, where each compartment containing 0, 1, or more target sequences is required for calculation. Interestingly, the compartmentalization of the sample efficiently enriches the target sequences within isolated microreactors. This enrichment effect reduces template competition and therefore enables the detection of rare mutations in a background of wild-type sequences (Quan et al., 2018, MDPI, "dPCR: A Technology Review").

[0118] dPCR also does not require nearly 100% amplification efficiency per cycle, as is necessary for qPCR, and therefore may be more resistant to inhibitors present in the sample. Instead, it is sufficient for the signal to be either detectable or undetectable at the end of the amplification reaction.

[0119] Like conventional PCR, dPCR also performs a single reaction within the sample, but the sample is separated into multiple compartments, and the reaction is performed individually in each compartment. This separation allows for more reliable collection and more sensitive measurement of nucleic acid levels.

[0120] dPCR involves compartmentalizing the PCR solution into at least several hundred, but most often thousands or tens of thousands or more, nanoliter-sized compartments, with separate PCR reactions occurring in each compartment. The dPCR solution is prepared in a similar manner to quantitative assays, using a PCR master mix containing either a probe, e.g., a fluorescent quencher probe, or an intercalation dye, along with DNA polymerase, dNTPs, MgCl2, and reaction buffer at optimal concentrations.

[0121] After multiple PCR amplification cycles, the sample is examined for fluorescence as a binary reading of "0" (not present) or "1" (present). The percentage of fluorescent compartments is recorded. Compartmentation of the sample allows for the estimation of the number of different molecules by assuming that the molecular population follows a Poisson distribution, thereby allowing for the consideration of the possibility of multiple target molecules being present in a single compartment.

[0122] In contrast to qPCR, dPCR is an endpoint PCR reaction. dPCR uses the number of fluorescently positive compartments relative to the total to calculate the target concentration. Unlike qPCR, a calibration curve is not required for sample quantification in dPCR. Overall, compared to qPCR, dPCR provides more robust quantification, is less susceptible to inhibitors, and is independent of quantification standards.

[0123] The advantages of dPCR include improved accuracy due to advanced sample compartmentalization, which ensures reliable measurement of the desired target thanks to its reproducibility. Furthermore, dPCR is highly quantitative because it does not rely on the relative fluorescence of the solution to determine the amount of amplified target nucleic acid.

[0124] According to the present invention, dPCR is performed in a plate. This offers several advantages compared to other dPCR methods, including the use of microfluidic chips and disks, microarrays, microdroplets, or droplet crystals based on oil-water emulsions.

[0125] In contrast to performing dPCR in a plate as in the present invention, droplet digital PCR (ddPCR) is a dPCR method in which, for example, a 20-microliter sample reaction containing assay primers and either a Taqman probe or an intercalation dye is divided into oil droplets of approximately 20,000 nanoliters in size by a water-oil emulsion technique, and subjected to thermal cycling to the endpoint in a 96-well PCR plate, with the fluorescence amplitude of each droplet read using a droplet flow cytometer. Each droplet must be measured individually.

[0126] In contrast, plate-based dPCR allows for faster readings because it can read all sections of the well (sample) simultaneously. Further important advantages of performing dPCR in plates, for example, nanoplates, include, but are not limited to: i) plates are easy for the user to use and familiar with, and pipetting is as easy as with qPCR, thus making them easy to apply even to users with little or no experience in using microfluidics; ii) fixed compartments prevent size variability and adhesion (which can occur in droplet-based dPCR), thereby maximizing consistency in the volume and number of compartments; iii) sealed (nano)plates prevent contamination between wells; iv) quality control is integrated, meaning that plate images can be seen for single fluorescence signals from individual positive compartment counts, so the user receives images as well as tables containing counts; v) plates are suitable for front-end automation, minimizing manual steps; vi) more volume can be loaded into the plate compared to droplet dPCR; vii) higher scalability in terms of loading volume and the number of compartments and wells.

[0127] Furthermore, with droplet digital PCR, the time it takes to deliver results is typically longer than with plate-based digital PCR. Additionally, the workflow and handling are more complex with droplet dPCR.

[0128] Furthermore, the chemical phenomena used in droplet dPCR differ from those used in plate-based dPCR. For droplet dPCR, a water-oil emulsion is required to enable droplet formation, and the mixture must also contain a stabilizing agent to prevent the droplets from falling apart after they are formed. In addition, the mixture must not contain any agents that interfere with the droplets. Plate-based dPCR, which uses a mechanical compartmentalization approach instead of a chemical approach, does not require any of these specific chemical requirements.

[0129] One consequence of the aforementioned features is the unprecedented technical effect that plate-based dPCR offers higher accuracy and precision than droplet-based dPCR, because each plate on which dPCR is performed provides the same conditions (e.g., the same number of compartments, size, and volume) for each analysis and each sample. This is different from droplets, which are unstable, vary in size, and can distort the distribution of target molecules. See also Kosir et al. ("Droplet volume variability as a critical factor for accuracy of absolute quantification using droplet digital PCR", Anal Bioanal Chem., 2017, DOI: 10.1007 / s00216-017-0625-y).

[0130] Furthermore, plate-based PCR offers the advantage of achieving higher and faster throughput because all compartments in a well can be read simultaneously.

[0131] Essentially, each compartment containing at least one molecule of the target of interest generates a signal, while compartments without the target molecule of interest do not generate a signal. Therefore, the number of positive and negative compartments can be used to calculate and quantify the concentration or amount of at least one nucleic acid target within at least one cell.

[0132] According to dMIQE Group and Huggett (dMIQE Group and Huggett, 2020, Clin Chem., "The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020"), two assumptions are necessary for dPCR to fit a Poisson distribution: all compartments have equal volume, and target molecules are randomly distributed throughout the compartments. In practice, this means that each compartment has an equal chance of containing target molecules. The number of target molecules present in a positive compartment can be one, two, or more, and at present, it is impossible to determine how many molecules a given positive compartment may contain. However, the number of molecules in a negative compartment is known. Assuming all compartments have equal volume, the mean concentration of target molecules per compartment (λ) can be estimated from the probability that a compartment is negative, using the proportion of negative compartments and a Poisson distribution. This concentration is derived from the number of negative compartments (w) and the total number of compartments in the reactant (n).

number

[0133] Quantification is impossible if all compartments contain amplification targets, i.e., if all compartments have a positive signal and the dPCR experiment is saturated.

[0134] Therefore, for exemplary dPCR systems utilizing 8,500, 26,000, or 22,000 compartments per well, the theoretical upper limit of the dynamic range is reached when 8,499 (=99.98%), 25,999 (=99.99%), or 21,999 (=99.99%) of each compartment contain at least one target molecule, i.e., an RNA molecule reverse-transcribed from one RNA target. Thus, for the dPCR systems described above, λ can be considered to be (rounded to the nearest integer) as follows:

number

[0135] The above λ values ​​represent the statistical / theoretical limits of the dPCR method.

[0136] In dPCR, the minimum amount of molecules required per volume of analyzed sample for one positive compartment is 1 (= the lowest detectable target concentration in a dPCR experiment).

[0137] In one embodiment, the quantitative result of at least one target is compared with the quantitative result of the same at least one target in at least one additional cell. This at least one additional cell also undergoes the same steps according to the method of the present invention as the one or more cells compared with this at least one additional cell. This at least one additional cell may originate from the same sample or from a different sample. This at least one additional cell may be of the same cell type or a different cell type. This at least one additional cell may have the same properties or different properties.

[0138] In one embodiment, the quantification result for at least one target is compared with the quantification result for the same at least one target in the cDNA and / or released DNA of at least one additional cell. This provides insight into the concentration of the target of interest in a single cell, a predetermined number of cells, cells exposed to different environments, or cells treated in a special manner. In any of these cases, the readings may not be corrected by many other cells present in an unspecified cell pool.

[0139] Generally, the “plate” according to the present invention is a microtiter plate known in the art. In one embodiment, the plate according to the present invention is a nanoplate. A nanoplate is a microtiter plate. Different plates can be used for different steps of the method, in particular the dissolution, reverse transcription reaction, and dPCR steps. Non-limiting examples include 96-well plates, 384-well plates, or nanoplates.

[0140] In one embodiment, the plate according to the present invention has at least 2 wells, or at least 6 wells, or at least 8 wells, or at least 12 wells, or at least 24 wells, or at least 48 wells, or at least 96 wells, or at least 192 wells, or at least 384 wells, or at least 768 wells, or at least 1536 wells, or 6 to 1536 wells, or 24 to 1536 wells, or 96 to 1536 wells, or 192 to 15 It has 36 wells, or 384 to 1536 wells, or 12 to 384 wells, or 12 to 96 wells, or 12 to 48 wells, or 24 to 384 wells, or 24 to 96 wells, or 24 to 48 wells, or 8 to 96 wells, or 2 to 384 wells, or 2 wells, or 6 wells, or 8 wells, or 12 wells, or 24 wells, or 48 wells, or 96 wells, or 192 wells, or 384 wells, or 768 wells, or 1536 wells.

[0141] In one embodiment, the plate according to the present invention has no compartments per well, or at least 10 compartments / well, or at least 100 compartments / well, or at least 500 compartments / well, or at least 1000 compartments / well, or at least 2000 compartments / well, or at least 3000 compartments / well, or at least 4000 compartments / well, or at least 5000 compartments / well, or at least 6000 compartments / well, or at least 7000 compartments / well, or at least 8000 compartments / well, or at least 8,500 plots / well, or at least 9,000 plots / well, or at least 10,000 plots / well, or at least 12,000 plots / well, or at least 14,000 plots / well, or at least 15,000 plots / well, or at least 18,000 plots / well, or at least 20,000 plots / well, or at least 22,000 plots / well, or at least 25,000 plots / well, or at least 26,000 plots / well, or at least 30,000 plots / well, or at least 50,000 plots / well , or 10-26000 sections / well, or 100-26000 sections / well, or 4000-26000 sections / well, or 8000-50000 sections / well, or 8000-30000 sections / well, or 8000-26000 sections / well, or 8000-25000 sections / well, or 8000-22000 sections / well, or 8000-20000 sections / well, or 8000-18000 sections / well, or 8000-15000 sections / well, or 8000-14000 sections / well, or 8000-120 00 sections / well, or 10000-50000 sections / well, or 10000-30000 sections / well, or 10000-26000 sections / well, or 10000-25000 sections / well, or 10000-22000 sections / well, or 10000-20000 sections / well, or 10000-18000 sections / well, or 10000-15000 sections / well, or 10000-14000 sections / well, or 10 sections / well, or 100 sections / well, or 500 sections / well, or 1000 sections / wellOr it has 2000 sections / well, or 3000 sections / well, or 4000 sections / well, or 5000 sections / well, or 6000 sections / well, or 7000 sections / well, or 8000 sections / well, or 8500 sections / well, or 9000 sections / well, or 10000 sections / well, or 12000 sections / well, or 14000 sections / well, or 15000 sections / well, or 18000 sections / well, or 20000 sections / well, or 22000 sections / well, or 25000 sections / well, or 26000 sections / well, or 30000 sections / well, or 50000 sections / well.

[0142] Increasing the number of sections increases sensitivity.

[0143] Any combination of the well volume and the compartment / well volume disclosed above is included within the scope of the present invention.

[0144] Generally, dPCR is performed on microtiter plates.

[0145] In one embodiment, the plate on which dPCR is performed has at least 2 wells, or at least 6 wells, or at least 8 wells, or at least 12 wells, or at least 24 wells, or at least 48 wells, or at least 96 wells, or at least 192 wells, or at least 384 wells, or at least 768 wells, or at least 1536 wells, or 6 to 1536 wells, or 24 to 1536 wells, or 96 to 1536 wells, or 192 to It has 1536 wells, or 384-1536 wells, or 12-384 wells, or 12-96 wells, or 12-48 wells, or 24-384 wells, or 24-96 wells, or 24-48 wells, or 8-96 wells, or 2-384 wells, or 2 wells, or 6 wells, or 8 wells, or 12 wells, or 24 wells, or 48 wells, or 96 wells, or 192 wells, or 384 wells, or 768 wells, or 1536 wells.

[0146] In one embodiment, the plate on which dPCR is performed has at least 10 compartments / well, or at least 100 compartments / well, or at least 500 compartments / well, or at least 1000 compartments / well, or at least 2000 compartments / well, or at least 3000 compartments / well, or at least 4000 compartments / well, or at least 5000 compartments / well, or at least 6000 compartments / well, or at least 7000 compartments / well, or at least 8000 compartments / well, or at least 8500 compartments / well, or a small number of compartments / wells. At least 9000 plots / well, or at least 10000 plots / well, or at least 12000 plots / well, or at least 14000 plots / well, or at least 15000 plots / well, or at least 18000 plots / well, or at least 20000 plots / well, or at least 22000 plots / well, or at least 25000 plots / well, or at least 26000 plots / well, or at least 30000 plots / well, or at least 50000 plots / well, or 10-26000 plots / Well, or 100-26000 sections / well, or 4000-26000 sections / well, or 8000-50000 sections / well, or 8000-30000 sections / well, or 8000-26000 sections / well, or 8000-25000 sections / well, or 8000-22000 sections / well, or 8000-20000 sections / well, or 8000-18000 sections / well, or 8000-15000 sections / well, or 8000-14000 sections / well, or 8000-12000 sections / well, or 100 00-50000 sections / well, or 10000-30000 sections / well, or 10000-26000 sections / well, or 10000-25000 sections / well, or 10000-22000 sections / well, or 10000-20000 sections / well, or 10000-18000 sections / well, or 10000-15000 sections / well, or 10000-14000 sections / well, or 10 sections / well, or 100 sections / well, or 500 sections / well, or 1000 sections / well, or 2000 sections / well,or having 3000 sections / well, or 4000 sections / well, or 5000 sections / well, or 6000 sections / well, or 7000 sections / well, or 8000 sections / well, or 8500 sections / well, or 9000 sections / well, or 10000 sections / well, or 12000 sections / well, or 14000 sections / well, or 15000 sections / well, or 18000 sections / well, or 20000 sections / well, or 22000 sections / well, or 25000 sections / well, or 26000 sections / well, or 30000 sections / well, or 50000 sections / well.

[0147] Increasing the number of sections increases sensitivity.

[0148] Any combination of the well volume and the compartment / well volume disclosed above is included within the scope of the present invention.

[0149] The selection of a plate (including the number of wells, the number of compartments per well, and the loading capacity) may depend particularly on the application, the desired throughput, and the availability of the sample. For infrequent events, a plate with more compartments per well, such as 26,000 compartments per well, may be beneficial. Similarly, for highly diluted targets in the sample, a plate with more compartments per well, such as 26,000 compartments per well, may also be beneficial. If high throughput is desired or required, a plate with more wells but potentially fewer compartments per well, such as a 96-well plate with 8,500 compartments per well, may be preferable.

[0150] In one embodiment, dPCR is performed in a nanoplate. Furthermore, reverse transcription and / or cell lysis can also be performed in a nanoplate.

[0151] Nanoplates are either 24-well or 96-well plates, meaning they can be loaded with up to 24 or 96 different samples, respectively. A 24-well nanoplate has approximately 8,500 or 26,000 compartments / well. A 96-well nanoplate has approximately 8,500 compartments / well. Typically, the sample loading capacity for a nanoplate with approximately 26,000 compartments / well is 40 μl, and for a nanoplate with approximately 8,500 compartments / well, it is 12 μl.

[0152] There is not much flexibility regarding reaction volume for a given volume of compartments / wells. If the volume is too small, the reaction may not be viable, and if the volume is too large, it may affect downstream priming, for example. Accurate determination of the sample volume undergone cycling is necessary to quantify the amount of at least one nucleic acid target in dPCR. Generally, the plate on which dPCR is performed provides fixed-size compartments that allow for accurate and reproducible concentration calculations. However, correction factors can be used for concentration calculations to compensate for even very small volume variations between different wells of the plate or between different plate batches.

[0153] One example of such correction factors is the volume precision factor (VPF) available for each plate when the QIAcuity® Digital PCR System is used. The VPF is a set of coefficients for each well. It consists of 96 individual coefficients that can address well-to-well variability and reduce variability between different mold configurations that result in batch-to-batch variability. This improves the accuracy of concentration measurements in dPCR, particularly in high-sensitivity applications such as the analysis of rare nucleic acid targets.

[0154] To achieve higher accuracy, multiple wells can be grouped together and analyzed as a single well (hyperwell). In analysis, a hyperwell can be treated as a single well but has more compartments. This can be useful for detecting rare events when the volume of sample being analyzed exceeds the volume that can be loaded into a single well. Alternatively or additionally, wells with more compartments can also be used for detecting rare events.

[0155] Further details of the results obtained by the method according to the present invention are described in detail in the legend and examples in the figures. [Examples]

[0156] The following examples illustrate the present invention. The amplified and detected seating positions should not be considered limiting to the scope of the present invention, but are merely illustrative seating positions that can be used within the scope of the present invention. (Example 1) Workflow for the analysis of coding RNA targets (mRNA)

[0157] The general workflow for this example is shown in Figure 1. Cells were directly sorted in lysis buffer using the cellenONE® cell sorting system. Reverse transcription and subsequent digital PCR were performed in plates to quantify at least one mRNA target. Detailed experimental procedures are described below. Cell culture, resuspension, and sample preparation:

[0158] HEK293 and HeLa cells were isolated 2 days after passage and cultured under standard conditions (DMEM / F12 containing 10% FBS, penicillin, streptomycin, and amphotericin-B, 37°C, 5% CO2). Prior to isolation, cells were washed twice with PBS, detached from the culture plate (0.5 ml trypsin, 37°C for 1 minute), centrifuged (250 × g, 4°C for 5 minutes), and resuspended in PBS (400 cells / μl). The cell suspension was stored on ice and diluted to 200 cells / μl in degassed PBS immediately before processing. Plate preparation:

[0159] Various 384-well microtiter plate (MTP) layouts with a specific number of cells per well (ranging from 1 to 100 cells / well) were defined using cellenONE® X1 software. For both cell samples, cell diameter and elongation parameters were defined to precisely isolate a specific number of single cells in each well.

[0160] A 384-well target plate was pre-filled with 10 μl / well of FastLane Cell One-Step Lysis Buffer (available as part of the FastLane Cell Probe Kit, catalog number 216413) and kept on ice until processing. Subsequently, for the detection and isolation of (single) cells, the pre-filled plate was transferred to a cellenONE® target holder (pre-cooled to 4°C). Instrument configuration and cell isolation:

[0161] Prior to isolation, small aliquots of the two cell line samples were processed to determine the optimal isolation parameters for each cell line (see Figure 2A). After completing the configuration, HEK293 and HeLa cells were isolated into three 384-well MTPs using the cellenONE® X1 system, according to the specified design layout as shown in Figure 2B. Successful isolation of cells:

[0162] Using cellenONE® technology, we successfully isolated cells, including single cells. Figure 3A shows an example of isolated HEK293 cells. After isolating all cells, a cellenREPORT was generated for each 384-well MTP, compiling all parameters and images of all isolated cells. The diameter and elongation parameters of the isolated cells were within the specified range (see Figures 3B and 3C).

[0163] Direct cell lysis and single-cell gene expression analysis were performed using the FastLane Cell Probe Kit (Qiagen, catalog number 216413) and the QIAcuity OneStep Advanced Probe PCR Kit (Qiagen, catalog number 250131) with the QIAcuity Digital PCR System (Qiagen, catalog number 911021). Generation of cell lysates:

[0164] Cell RNA lysates were prepared using the FastLane Cell One-Step Buffer Set (available as part of the FastLane Cell Probe Kit, catalog number 216413). For this purpose, 47.6 μl of buffer FCPL was mixed with 2.4 μl of gDNA Wipeout Buffer 2. This buffer, in addition to lysing cells, stabilizes cellular RNA and genomic DNA, eliminating the need for RNA purification. Cells were directly isolated in the wells of a 384-well MTP plate containing 10 μl of this Fast Lane Lysis Buffer. The plate was then incubated at ambient temperature for 5 minutes, followed by heating to 75°C on a thermoblock (QINSTRUMENTS ColdPlate Slim version, catalog number 2016-0111) for 5 minutes. The plate was then frozen at -80°C and stored until further processing was required. Reverse transcription digital PCR (RT-dPCR):

[0165] The reaction was set up according to the standard QIAcuity OneStep Advanced Probe Kit Quick Start Protocol. 2 μl of cell lysate was directly incorporated into the RT-dPCR reaction mixture for each condition. The total volume of the reaction mixture was 12 μl. A TaqMan probe-based assay was used for multiple detection of target gene expression levels. Subsequently, the RT-dPCR reaction mixture was pipetted into the wells of a QIAcuity 8.5k nanoplate. The nanoplate was sealed and placed in a QIAcuity Digital PCR instrument (Series 0; R&D version) according to the instrument's user manual. A standard QIAcuity One Step RT-dPCR cycling program was selected. The results were analyzed using the QIAcuity Software Suite (Suite 2.0.20, CSW 2.0.0.144). High-sensitivity detection of absolute transcript copies at the single-cell level.

[0166] Using the workflow outlined in Figure 1, transcription levels of various targets in HEK293 and HeLa cells were analyzed. As shown in Figures 4A (HEK293 cells) and 4B (HeLa cells), differences in target gene expression levels across different cell lines can be easily investigated in a high-throughput manner. Cell sorting and subsequent cell lysis allow for the use of a precise number of cells in the RT-dPCR reaction. High-quality cell sorting, lysate preparation, and RT-dPCR enable optimal cell analysis over a wide linear range (Figures 4A and 4B). Furthermore, multiple targets can be analyzed simultaneously (Figure 5). Notably, thanks to the low detection limit in dPCR, transcripts present in low intracellular abundances can still be accurately detected. Thanks to lysate compartmentalization and endpoint PCR, dPCR enables highly sensitive detection of absolute copies of transcripts at the single-cell level (Figure 6). Variation in target gene expression levels from one individual cell to another can also be investigated in an absolutely high-throughput manner using the method according to the present invention (Figures 7A-C).

[0167] Thus, the workflow of the present invention achieves high-throughput absolute quantification of low-abundance targets at the single-cell level. Real-time, 100% accurate single-cell isolation without compromising viability and transcript expression allows for the use of a precise number of intact cells for RT-dPCR reactions. Furthermore, the absence of an RNA purification step significantly reduces working time. In addition, multiplexing can be performed in a one-step RT-dPCR format.

[0168] In summary, this simple yet efficient workflow, which combines the isolation of a specified number of cells with absolute gene expression analysis, yields highly sensitive and reproducible linear quantification at the transcript level in cell lysates. (Example 2) Workflow for DNA (genomic DNA and / or mtDNA) analysis

[0169] The general workflow for this embodiment is shown in Figure 8. Cells were directly sorted in a lysis buffer without DNAseI using the cellenONE® cell sorting system. Digital PCR was performed on plates to analyze CNV or mitochondrial copy number. Detailed experimental procedures are described below. Cell culture, resuspension, and sample preparation:

[0170] HEK293 and HeLa cells were isolated 2 days after passage and cultured under standard conditions (DMEM / F12 containing 10% FBS, penicillin, streptomycin, and amphotericin-B, 37°C, 5% CO2). Prior to isolation, cells were washed twice with PBS, detached from the culture plate (0.5 mL trypsin, 37°C for 1 minute), centrifuged (250 g, 4°C for 5 minutes), and resuspended in PBS (400 cells / μL). The cell suspension was stored on ice and diluted to 200 cells / μL in degassed PBS immediately before processing. Plate preparation:

[0171] Target 384 MTPs were pre-filled with 3 μl / well of FastLane Cell One-Step Lysis Buffer (available as part of the FastLane Cell Probe Kit, catalog no. 216413; without DNase I) and kept on ice until processing. Subsequently, for (single) cell detection and isolation, the pre-filled plates were transferred to cellenONE® target holders (pre-cooled to 4°C). Plate layout:

[0172] As shown in Figure 9, several different layouts were designed and configured in the cellenONE® X1 software before cell sorting. One 384 MTP layout was designed using 384 single cells (1 cell per well), and a second 384 MTP layout was designed using a different number of cells per well (ranging from 1 to 100 cells per well). Instrument configuration and single-cell isolation:

[0173] Prior to isolation, small aliquots of different cell samples were processed to determine the optimal isolation parameters for each cell line, as shown in Table 1. [Table 1]

[0174] For both cell samples (HEK293 cells and HeLa cells), a specific number of cells were precisely isolated into each well, with only cell diameter and elongation parameters specified. After completing the configuration, HEK293 and HeLa cells were isolated in three 384 MTP wells using the cellenONE® X1 system, according to the layout specified in Figure 9. The actual parameters of the sorted cells are shown in Table 2. [Table 2]

[0175] The cells were directly isolated in Fast-Lane lysis buffer (see below). Direct cell lysis and single-cell targeted copy number analysis using the QIAcuity Probe PCR Kit on the QIAcuity Digital PCR System: Generation of cell lysates:

[0176] DNA lysates from cells were prepared using the Fast Lane Cell One Step Buffer Set without the addition of DNase I. Cells were directly isolated into the wells of a 384-well MTP plate containing 3 μl of Fast Lane Lysis Buffer (without DNase I). The plate was then incubated at ambient temperature for 5 minutes, followed by heating to 75°C for 5 minutes to thermally inactivate the lysis buffer (QInstrument ColdPlate Slim; 2016-0111). The plate was then frozen at -80°C until further processing was required. Successful isolation of single cells:

[0177] An example of isolated HeLa cells is shown in Figure 10A. After isolating all cells, a cellenREPORT was generated for each of the 384 MTPs, compiling all parameters and images of all isolated cells. The diameter and elongation parameters of the isolated single cells were within the specified range (Table 2 and Figure 10B). Digital PCR:

[0178] The dPCR reaction was set up according to the standard QIAcuity dPCR Probe CNV Assays Quick Start Protocol (HB-3060). A total volume (3 μl) of cell lysates containing varying amounts of cells was directly incorporated into the dPCR reaction mixture for each condition. QIAcuity dPCR probe CNV assays and TaqMan probe-based assays were used for multiple detection at the target copy number level at the gDNA and mtDNA levels, respectively. Subsequently, the dPCR reaction mixture was pipetted into the wells of QIAcuity 8.5k or 26k nanoplates. The nanoplates were sealed and placed in the QIAcuity Digital PCR instrument (Series 0) according to the instrument's user manual. A standard QIAcuity dPCR probe CNV assay cycling program was selected. The results were analyzed using the QIAcuity Software Suite (Suite 2.0.20 and 2.1.7.187). High-sensitivity analysis of genomic DNA (gDNA) and mitochondrial DNA (mtDNA) copy number variations at both the single-cell and population levels:

[0179] The workflow outlined above (see also Figure 8) was used to investigate the copy numbers of various genomic and mtDNA targets in HEK293 or HeLa cells. Cell sorting before cell lysis ensures that a precise number of cells are loaded into the dPCR reaction. High-precision cell sorting, efficient lysate preparation, and highly sensitive dPCR detection enable optimal copy number analysis in single cells and cell populations. According to the method of the present invention, multiple targets within a cell can be analyzed simultaneously in a single reaction. A major challenge associated with multiple copy number analysis is that different targets may have different copy numbers. Many gDNA targets exist as two copies in a healthy diploid genome, while mtDNA targets may have a much higher copy number than gDNA targets within the same cell. Simultaneous detection of such variable copy number ranges requires not only high sensitivity but also a wide detection dynamic range. Thanks to lysate compartmentalization and endpoint PCR, dPCR provides high-resolution, highly sensitive detection of absolute copies of low-copy and high-copy targets at the single-cell level (Figures 11A and B).

[0180] In addition, the wide dynamic range of the method according to the present invention enables accurate reading of copy number changes in high-copy targets as cell loading density increases (Figure 12). Therefore, copy number changes in various targets with low, medium, and high copy numbers can be detected in a highly accurate and reproducible manner in a single multiplexed reaction. Furthermore, differences in the copy number of mtDNA or gDNA targets in different cell types can also be analyzed in a high-throughput manner using the method according to the present invention (Figure 13). Apart from the study of cell populations, single-cell heterogeneity can also be examined for gDNA and mtDNA copy numbers in an absolutely high-throughput manner using this workflow (Figure 14; showing mtDNA single-cell heterogeneity).

[0181] In summary, this simple yet efficient workflow, combining cell sorting with dPCR and copy number analysis, provides high-throughput, highly sensitive, and reproducible linear quantification of targeted genomic or mitochondrial DNA copy numbers in cell lysates.

[0182] The method according to the present invention achieves high-throughput absolute quantification of genomic DNA targets and mitochondrial DNA targets, even at the single-cell level. Rapid, real-time, and 100% accurate single-cell isolation enables the use of a precise number of intact cells for dPCR reactions. Furthermore, the use of lysis buffers that stabilize RNA and DNA, eliminating the need to purify the nucleic acids of interest, significantly reduces working time. In addition, the method according to the present invention enables target multiplexing in a single dPCR reaction with little to no optimization. Taken together, this simple yet efficient workflow, combining cell sorting with dPCR and copy number analysis, results in highly sensitive, reproducible, and linear quantification of target genomes or mitochondrial copy numbers in cell lysates. (Example 3) Workflow for the analysis of small non-coding RNA targets (e.g., miRNAs)

[0183] Figure 15 shows the general workflow for this embodiment. Cells were directly sorted in lysis buffer using the cellenONE® cell sorting system. Reverse transcription and digital PCR were performed in plates, and target miRNAs were analyzed and quantified. Detailed experimental procedures are described below. Cell culture, resuspension, and sample preparation:

[0184] Immortalized HEK293 cells were isolated 2 days after passage and cultured under standard conditions (DMEM / F12 containing 10% FBS, penicillin, streptomycin, and amphotericin-B, 37°C, 5% CO2). Prior to isolation, cells were washed twice with PBS, detached from the culture plate (0.5 ml trypsin, 37°C for 1 minute), centrifuged (250 × g, 4°C for 5 minutes), and resuspended in PBS (400 cells / μL). The cell suspension was stored on ice and diluted to 200 cells / μL in degassed PBS immediately before processing. Plate preparation:

[0185] Using the FastLane Cell Probe Kit, 47.6 μl of FCPL buffer was mixed with 2.4 μl of gDNA Wipeout Buffer 2. Target 384 MTPs were pre-filled with 10 μL / well of this FastLane Cell One-Step Lysis Buffer (available as part of the FastLane Cell Probe Kit, catalog no. 216413) and kept on ice until processing. Subsequently, for single-cell detection and isolation, the pre-filled plates were transferred onto cellenONE® target holders (pre-cooled to 4°C). Cell isolation:

[0186] A specific 384 MTP layout with a defined number of cells ranging from 300 to 1 cell per well was prepared using cellenONE® X1 software (Figure 16). Prior to isolation, small aliquots of cells were processed to define the optimal isolation parameters for HEK293 cells. A specific number of cells were precisely isolated into each well using only cell diameter and elongation parameters. Optimal parameters: minimum diameter 16.03 μm and maximum diameter 30 μm. 1.60 μm was used as the maximum elongation parameter. After completing the configuration, HEK293 cells were isolated into the wells of the 384 MTP using the cellenONE® X1 system according to the layout defined in Figure 16. Direct cell lysis and miRNA analysis using the QIAcuity EG PCR kit on the QIAcuity dPCR system: Generation of cell lysates:

[0187] Cellular RNA lysates were prepared using the Fast Lane Cell One-Step Buffer Set (available as part of the Fastlane Cell Probe Kit, catalog number 216413). This buffer not only lyses cells but also stabilizes cellular RNA and genomic DNA, eliminating the need for RNA purification. Cells were directly isolated into the wells of a 384-well MTP plate containing 10 μl of Fast Lane Lysis Buffer. The plate was then incubated at ambient temperature for 5 minutes, followed by heating to 75°C on a thermoblock for 5 minutes. The plate was then frozen at -80°C and stored until further processing was required. miRNA polyadenylation and reverse transcription using the miRCURY LNA RT kit:

[0188] Cell lysates were used directly as input to the miRCURY LNA RT reaction. The reaction mixture for one reaction consisted of 10 μL of cell lysates, 4 μL of 5× miRCURY RT SYBR Green reaction buffer, 2 μL of miRCURY enzyme mixture, and 4 μL of water. The reaction mixture was thoroughly mixed and incubated at 42°C for 60 minutes. During this step, polyadenylation and reverse transcription of miRNA occurred. The reaction mixture was then incubated at 95°C for 5 minutes. The cDNA was stored at -20°C until further use. miRNA quantification using dPCR:

[0189] Before setting up the dPCR reaction, the cDNA was diluted 7.5-fold with water. QIAcuity EG PCR mix was used for dPCR. The master mix and the miRCURY LNA miRNA PCR assays hsa-miR-10a-5p and hsa-miR-10b-5p were used at 1× final concentration. No additional water was added to the final reaction product (66% of the template in the dPCR reaction product). After preparing the reaction mixture in a pre-plate, it was transferred to either an 8.5k or 26k nanoplate. The plate was sealed and placed in the QIAcuity dPCR instrument according to the instrument's user manual. miRCURY dPCR standard cycling was selected according to the handbook. Results were analyzed using the QIAcuity Software Suite (Suite 2.1.8). High-sensitivity detection of absolute miRNA copies at the single-cell level.

[0190] The expression levels of the hsa-miR-10a-5p and hsa-miR-10b-5p targets in HEK293 cells were analyzed. As shown in Figure 17 and Figures 18A, 18B, and 18C, both miRNAs could be detected with high linearity from a cell pool containing 300 cells down to the single-cell level. Therefore, the method according to the present invention enables the analysis of miRNAs derived from different cell lines and the analysis of different cell pool sizes in a high-throughput manner.

[0191] The rapid, real-time, and 100% accurate single-cell isolation method of the present invention enables the use of a precise number of intact cells for RT and dPCR reactions. High-quality cell isolation, lysate preparation, RT, and dPCR allow for optimal cell analysis over a wide linear range (Figures 17 and 18A, 18B, and 18C). Even miRNAs present in low intracellular abundance can be accurately detected due to the low detection limit of dPCR. In addition, thanks to lysate compartmentalization and endpoint PCR, dPCR enables highly sensitive detection of absolute copies of miRNAs, even at the single-cell level. Variation in miRNA levels in individual cells can also be investigated in an absolutely high-throughput manner using the method according to the present invention. Furthermore, the absence of an RNA purification step significantly reduces working time.

[0192] In summary, this simple yet efficient workflow, which combines precise cell isolation into lysis buffer with absolute miRNA quantification, results in highly sensitive and reproducible linear quantification from cell lysates used as input for RT and dPCR. (Example 4) A workflow for target analysis in which cell lysis and dPCR are performed in the same plate.

[0193] To further enhance the time and cost efficiency of the present invention, intact cells can be loaded directly onto a plate containing lysis buffer, and the plate can later be used for dPCR. An exemplary workflow for this is described below.

[0194] Preload 10 μl of CaseWork Go Lysis Buffer (Qiagen, item number 1116186) into a 24-well 26k dPCR plate. Add 2 μl of the prepared cell suspension to the pre-loaded lysis buffer (PBS, 10 μl per well). 4 Load (~1 cell) onto the plate. Seal the plate with Airpore foil (Qiagen, item number 1017662) and incubate at room temperature for 10 minutes.

[0195] If necessary, i.e., if proteinase K is present in the lysate, the sample is thermally inactivated after the previous step, for example, at 75°C for 5 minutes. If proteinase K is present, the plate can be sealed with, for example, Airpore foil (Qiagen, item no. 1017662) and incubated, for example, in a constant temperature incubator. The sheet prevents a) contamination and b) evaporation (at lower temperatures). In contrast to qPCR cover foil which can be used similarly, the coagulation is not considered to be coagulation on the foil.

[0196] The plate is then opened, and 28 μl of the pre-prepared master mix (see, for example, Table 3 below) is added and mixed with the lysate by pipetting up and down (avoiding bubble formation). The plate is sealed with Nanoplate Seal (Qiagen) and dPCR is performed. Exemplary cycling and imaging conditions are shown in Tables 4 and 5 below, respectively.

[0197] [Table 3]

[0198] [Table 4]

[0199] [Table 5]

[0200] We conducted three different experiments using the general workflow described above. These are described in more detail in sections 4.1 to 4.3 below. (Example 4.1) Use of 1 to 100,000 cells

[0201] The following dPCR layout was used for this experiment: [Table 6]

[0202] The master mix was structured as follows: [Table 7-1] [Table 7-2]

[0203] The protocol was as follows: [Table 8-1] [Table 8-2]

[0204] The dPCR parameters were as follows: [Table 9]

[0205] (Example 4.2) Use of 0.58 to 580 cells The following dPCR layout was used for this experiment. [Table 10-1] [Table 10-2]

[0206] The master mix was structured as follows: [Table 11-1] [Table 11-2]

[0207] The protocol was as follows: [Table 12]

[0208] The dPCR parameters were as follows: [Table 13]

[0209] (Example 4.3) Use of 0.9 to 900 cells The following dPCR layout was used for this experiment. [Table 14]

[0210] The master mix was structured as follows: [Table 15-1] [Table 15-2]

[0211] The protocol was as follows: [Table 16-1] [Table 16-2]

[0212] The dPCR parameters were as follows: [Table 17]

[0213] Figure 19 is a quantitative table showing the results of the experiments of Example 4.1 (Figure 19A), 4.2 (Figure 19B), and 4.3 (Figure 19C).

[0214] Figure 20 is a scatter plot showing the results of the experiments of Example 4.1 (Figure 20A), 4.2 (Figure 20B), and 4.3 (Figure 20C).

[0215] Figure 21 is a graph showing the results of all the experiments of Example 4.1 to 4.3.

Brief Description of the Drawings

[0216] [Figure 1] Rationalized (single-cell) gene expression analysis workflow.

[0217] [Figure 2-1] (Single) cell isolation parameters and plate layouts for (single) cell isolation for exemplary cell lines HeLa and HEK293. A) Optimal (single) cell isolation parameters for HeLa cells and HEK293 cells. B) Display of three different layouts for cell isolation using the cellenONE® X1 system. (A) 384-well MTP layout for the isolation of one single HeLa cell per well, (B) 384-well MTP layout for the isolation of one single HEK293 cell per well, (C) 384-well MTP layout for the isolation of various numbers of HeLa cells and HEK293 cells per well. [Figure 2-2] The same as above.

[0218] [Figure 3-1]Single cells isolated using cellenONE® X1 technology. A) Image showing a single HEK293 cell inside the capillary efflux area before isolation. The coordinates of the well (A1) and the parameters of the isolated cell (D: diameter (22.77 μm), E: elongation (1.35 μm), I: gray intensity (70.51)) are shown at the top of the image. To the right of the cell, two vertical lines are shown in the figure. They "demarcate" the sedimentation area, which is the safety zone. If this area contains at least one additional cell, the cells in the efflux area (shown herein as the area to the left of the sedimentation area) are discarded. B) Scatter plot of HEK293 and HeLa isolations generated by the cellenONE® X1 analysis module, showing cell elongation and cell diameter in comparison. All isolated cells are represented by individual dots in the darkest gray shading. C) Parameters of the isolated (single) cell. [Figure 3-2] Same as above.

[0219] [Figure 4-1] Analysis of gene expression levels in different cell lines at various cell densities. Results were obtained using the ACTB-FAM assay in RT-dPCR for HEK293 and HeLa cell lines. A) Analysis of gene expression levels in HEK293 cells. Left: Each dot represents a positive compartment; shows the fluorescence intensity of the positive compartment per well for various numbers of analyzed cells. Right: Graph showing the concentration of the analyzed target (shown as copies / μl) according to the number of analyzed cells. B) Analysis of gene expression levels in HeLa cells. Left: Each dot represents a positive compartment; shows the fluorescence intensity of the positive compartment per well for various numbers of analyzed cells. Right: Graph showing the concentration of the analyzed target (shown as copies / μl) according to the number of analyzed cells. [Figure 4-2] Same as above.

[0220] [Figure 5]Simultaneous quantification of low, medium, and high-abundance targets in cell lysates during multiplexed RT-dPCR reactions. HeLa cell lysates with high cell density were loaded. ACTB-FAM, MYC-ROX, and KDR-HEX assays were used for multiplexing. Each dot represents a positive compartment. Fluorescence intensity of positive compartments per well is shown for various numbers of analyzed cells.

[0221] [Figure 6] High-sensitivity analysis of gene expression levels in single cells. Target ACTB transcripts were detected in single HEK293 cells (shown as dots) using the ACTB-FAM assay in one-step RT-dPCR (C and D, respectively). Transcripts were not detected in non-template controls (NTC) (A and B). Positive compartments are highlighted in this figure using white arrows pointing to the fluorescent compartments. Each subfigure represents a well with 8500 compartments.

[0222] [Figure 7-1] Multiplex analysis of gene expression levels in single cells. Single-cell (HeLa) lysates and non-template controls (NTCs; for each plate, the last two regions of the last row are NTCs) were loaded onto 8.5k nanoplates and tested using ACTB-FAM, MYC-ROX, and CDK2NA-HEX assays. A) ACTB-FAM assay. B) MYC-ROX assay. C) CDK2NA-HEX assay. [Figure 7-2] Same as above. [Figure 7-3] Same as above.

[0223] [Figure 8] A streamlined workflow for (single-cell) genomic CNV and / or mitochondrial copy number analysis.

[0224] [Figure 9]Display of two plate layouts used for cell isolation with the cellenONE® X1 system. Left: 384-MTP layout for isolation of a single cell per well. Right: 384-MTP layout for isolation of various numbers of cells per well (0, 1, 2, 5, 10, 25, 50 or 100 cells per well), magnifications for wells A1-8, B1-8, C1-8, D1-8, E1-8, F1-8, G1-8 and H1-8 are shown.

[0225] [Figure 10-1] Single-cell isolation using cellenONE® X1 technology. A) Image showing a single HeLa cell inside the capillary efflux area before isolation. The coordinates of the well (2) and the parameters of the isolated cell (D: diameter (25.79 μm), E: elongation (1.54), I: gray intensity (71.50)) are shown at the top of the image. To the right of the cell, two vertical lines are shown in the figure. They "demarcate" the sedimentation area, which is the safety zone. If this area contains at least one additional cell, the cell in the efflux area is discarded. B) Example of a scatter plot generated by the cellenONE® X1 analysis module, showing cell elongation and cell diameter in comparison. All isolated cells are represented by individual points in the darkest gray shading. Left: Scatter plot of HeLa isolation. Right: Scatter plot of HEK293 isolation. [Figure 10-2] Same as above.

[0226] [Figure 11]Genomic DNA (gDNA) and mitochondrial DNA (mtDNA) targets exhibit different copy numbers at the single-cell level. The difference in copy numbers can be depicted with high resolution using a 1D scatter plot or a signal map in the QIAcuity Software Suite. The results were obtained in a single multiplex digital PCR reaction loaded onto an 8.5k nanoplate using A) the ND4-FAM, RPP30-HEX, and SPIN4-ROX assays, and B) the CYB-FAM, RPP30-HEX, and SPIN4-ROX assays. The SPIN4 (X chromosome) and RPP30 (chromosome 10) assays target gDNA and are expected to be present at 2 copies / genome in healthy wild-type cells. The ND4 and CYB assays target mtDNA and are expected to be present at high copy numbers. A) A 1D scatter plot where points above the horizontal threshold line correspond to partitions and show positive fluorescence signals after amplification of the indicated targets in a single HEK293 cell. Well numbers are shown above. B) A signal map showing positive partitions for the indicated assays in a single HEK293 cell. Positive partitions are highlighted in this figure using white arrows pointing to fluorescent partitions.

[0227] [Figure 12] Simultaneous quantification of low, medium, and high copy number targets in HEK293 cell lysates. Cell lysates with increased cell density were loaded into the reactions. Triplicate samples from each individual loading condition are shown. Results were obtained using the TERT-ROX, AMY1A-FAM, and ND4-Cy5 assays in a multiplex digital PCR reaction loaded onto a 26k nanoplate. TERT (chromosome 5, low copy number) and AMY1A (chromosome 11, medium copy number) are genomic DNA targets, while ND4 (high copy number) is a mitochondrial DNA target.

[0228] [Figure 13]This study demonstrates the linearity of detection for copy numbers of genomic and mitochondrial DNA targets when the number of HEK293 or HeLa cells per reaction is increased. The AMY1A-FAM, TERT-ROX, and ND4-Cy5 assays were used in multiplex digital PCR reactions loaded into 26k nanoplates. AMY1A (chromosome 11, medium copy number) and TERT (chromosome 5, low copy number) are genomic DNA targets, while ND4 (high copy number) is a mitochondrial DNA target. Results represent the average copies / μl obtained from three replicate samples. R² > 0.97 for all conditions.

[0229] [Figure 14] Mitochondrial DNA copy number analysis using assays targeting CYB and ND4 demonstrates heterogeneity in mitochondrial DNA copying within single cells. Column "A": HEK293, and Column "B": HeLa single-cell lysates were loaded onto 8.5k nanoplates and tested using CYB-FAM or ND4-FAM assays.

[0230] [Figure 15] A streamlined (single-cell) miRNA analysis workflow.

[0231] [Figure 16] Plate layouts for cell isolation using the cellenONE® X1 system. Cells were isolated in pools containing 300 cells / well, 150 cells / well, 75 cells / well, 35 cells / well, 5 cells / well, 2 cells / well, or a single cell per well. Enlarged views of wells A1-4, B1-4, C1-4, D1-4, E1-4, F1-4, G1-4, H1-4, and I1-4 are shown.

[0232] [Figure 17]Analysis of miRNA quantification in various numbers of sorted cells. Cells were sorted and lysed at various cell densities, and miRNAs were reverse transcribed and quantified by dPCR on 26k nanoplates. The miRCURY LNA miRNA PCR assay targeting hsa-miR-10b-5p was used for quantification.

[0233] [Figure 18-1] High-sensitivity analysis of miRNAs for HEK293 cell infusions across a wide range. A) High linearity of miRNA expression across a range from 300 selected cells to single cells. miRCURY LNA miRNA PCR assays hsa-miR-10a-5p and hsa-miR-10b-5p were used. Quantified copies / μl were plotted against cell number for each target. B) High linearity of miRNA expression across a range from 300 selected cells to single cells. miRCURY LNA miRNA PCR assay hsa-miR-10b-5p was used. Quantified copies / μl were plotted against cell number for each target. C) Signal maps for various cell count infusions showing positive compartments (fluorescent dots in the figure) for various cell counts. miRCURY LNA miRNA PCR assay hsa-miR-10b-5p was used. [Figure 18-2] Same as above.

[0234] [Figure 19-1] Quantitative results of genomic DNA across a wide range of Jurkat and HeLa cell introductions. A) Quantitative results of the experiment outlined in Example 4.1. 1 to 100,000 (1:10 dilution) Jurkat cells were analyzed in three repeated samples to quantify the amounts of MYC and RPP (30 copies / μl). B) Quantitative results of the experiment outlined in Example 4.2. 0.58 to 580 (1:10 dilution) HeLa cells were analyzed in three repeated samples to quantify the amounts of MYC and RPP (30 copies / μl). C) Quantitative results of the experiment outlined in Example 4.3. 0.9 to 900 (1:10 dilution) HeLa cells were analyzed in three repeated samples to quantify the amounts of MYC and RPP (30 copies / μl). [Figure 19-2] Same as above. [Figure 19-3] Same as above. [Figure 19-4] Same as above. [Figure 19-5] Same as above. [Figure 19-6] Same as above.

[0235] [Figure 20-1] Scatter plots showing fluorescence intensity measured for genomic DNA targets in Jurkat cell lysates and HeLa cell lysates. A) Jurkat cell lysates with reduced cell density were loaded into the reactant. Three replicate samples from each individual loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiment is described in Example 4.1. B) HeLa cell lysates with reduced cell density were loaded into the reactant. Three replicate samples from each individual loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiment is described in Example 4.2. C) HeLa cell lysates with reduced cell density were loaded into the reactant. Three replicate samples from each individual loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiment is described in Example 4.3. [Figure 20-2] Same as above. [Figure 20-3] Same as above.

[0236] [Figure 21] Figure 19 shows a graph illustrating the quantitative results for all experiments described in Examples 4.1 to 4.3.

Claims

1. A method for quantifying the amount of at least one nucleic acid target, wherein the method is: e) A step of providing a specified number of cells, wherein the specified number of cells is at least one cell; f) Lysing at least one of the cells, thereby releasing RNA and DNA; g) a step of performing a reverse transcription reaction on the released RNA to generate complementary DNA (cDNA), and further performing a digital polymerase chain reaction (dPCR) on at least one reverse-transcribed RNA target in the cDNA; and / or A step of performing a digital polymerase chain reaction (dPCR) on at least one DNA target in the released DNA, wherein the dPCR is performed in a plate; and h) A step of quantifying the amount of the at least one nucleic acid target. Methods that include...

2. The method according to claim 1, wherein the at least one of the provided cells is directly isolated in a lysis buffer.

3. The method according to any one of the prior claims, wherein each of the at least one nucleic acid targets is independently selected from coding RNA targets; non-coding RNA targets; genomic DNA targets; and mitochondrial DNA targets.

4. The method according to claim 3, wherein the coding RNA target is mRNA; the non-coding RNA target is miRNA; the genomic DNA target is a region on genomic DNA that harbors or is suspected of harboring copy number variations (CNV); and the mitochondrial DNA target is a region on mitochondrial DNA that harbors or is suspected of harboring CNV.

5. The method according to any of the prior claims, wherein the at least one cell is provided by a method selected from the group consisting of fluorescence-activated cell sorting, micromanipulation, microfluidics, immunopanning, magnetoactivated cell sorting, and laser microdissection.

6. The method according to any one of the prior claims, wherein the dPCR is performed in a nanoplate.

7. The method according to any of the prior claims, wherein the dPCR is performed simultaneously against one, two, three, four, or five targets in the cDNA and / or the released DNA.

8. The method according to any one of the prior claims, wherein each of the at least one nucleic acid targets is independently selected from the group consisting of low-copy targets, medium-copy targets, and high-copy targets.

9. The method according to any one of the prior claims, wherein quantification is based on the detection of at least one nucleic acid target amplified by using at least one probe for each target to be analyzed, the at least one probe specifically binds to each of the amplification products for each nucleic acid target.

10. The method according to claim 9, wherein at least one of the probes is fluorescently labeled.

11. The method according to claim 10, wherein the at least one probe is at least one TaqMan probe.

12. The method according to any of the prior claims, wherein the quantification result of the at least one target is compared with the quantification result of the at least one target in the cDNA and / or the released DNA of at least one further cell.

13. The method according to any one of the prior claims, wherein the at least one cell is derived from a eukaryotic sample, preferably a human sample.

14. The method according to claim 13, wherein the human sample is derived from a subject that has cancer or is suspected of having cancer.

15. The method according to claim 13, wherein the human sample is derived from a subject having or suspected of having a disease.

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