High accuracy quantification of nucleic acids from predetermined quantities of cells using substrate-based digital PCR

Abstract

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

Description

Technical Field

[0001] This invention belongs to the field of molecular biology, particularly the field of targeted nucleic acid analysis. More specifically, this invention belongs to the field of mRNA and miRNA expression analysis and targeted genomic and mitochondrial copy number variation analysis, wherein a specific number of cells are isolated and nucleic acids are quantified using plate-based digital PCR. Background Technology

[0002] Compared to traditional methods of analyzing cell populations in batches, single-cell analysis allows researchers to uncover new information and gain new insights into the biological processes of individual cells. Therefore, it allows for the analysis of 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, dramatic intercellular variations can be observed, particularly at the transcriptomic level. Capturing this transcriptomic heterogeneity is especially important for disease and drug development research, where differences in the biological responses of individual cells to drugs compared to tissues can provide additional insights into disease progression or prevention. Complex transcriptional networks can be mapped to generate improved models of drug responses. Beyond disease and drug screening therapies, single-cell transcriptomic analysis can also be applied to areas such as aging, stem cells, gene and cell therapies, prenatal screening, organoid research, and infectious disease research.

[0004] Capturing cellular heterogeneity is also important for detecting naturally occurring intracellular mutations, chromosomal rearrangements, or copy number variations (CNVs; or copy number alterations (CNAs)). CNVs are structural changes in the genome (e.g., deletions, insertions, duplications, translocations, and inversions) that result in copy number increases or losses ranging from a few base pairs to hundreds of base pairs or even entire regions of the chromosome. CNVs are either inherited or the result of changes in newborn somatic cells. CNVs account for up to 10–20% of the variation in the genome and are a source of natural genetic diversity and biological dysfunction in humans. CNVs commonly lead to gene dysfunction, dosage imbalances, chromosome shortening, and positional effects, and they are associated with complex diseases and traits such as cancer, obesity, aging, neurodegenerative diseases, and autoimmune diseases. Therefore, quantitative analysis of CNVs at disease-associated sites can provide insights into the molecular mechanisms of disease and has the potential to discover new biomarkers.

[0005] Similar to changes in genomic DNA copy number (gDNA CN), variations in mitochondrial DNA copy number (mtDNA CN) in blood or tissues have been associated with disease, which is not surprising given the crucial role of mitochondria in cellular homeostasis. In contrast to the fixed copy number of genomic DNA (the healthy diploid state), dramatic intercellular variability can be observed at the mitochondrial DNA (mtDNA) level, even among cells that are genetically and morphologically identical. mtDNA copy number fluctuates and varies between individuals, different tissues, cells, and even within the same cell's mitochondria. Individual cells can carry hundreds to hundreds of thousands of mtDNA copies. However, drastic changes in mtDNA copy number can lead to mitochondrial dysfunction and disease development. Therefore, similar to genomic CNV, capturing mtDNA copy number heterogeneity is important for disease and drug development research and can be used as an indicator for screening metabolic diseases, cancer, prenatal testing, neurodegenerative diseases, and age-related diseases.

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

[0007] Furthermore, non-coding RNAs actively participate in cellular function and specialization. For example, miRNAs are important, conserved non-coding post-transcriptional regulators with great potential as diagnostic or prognostic biomarkers. The latest miRbase release lists 2,654 annotated human miRNAs. Dramatic intercellular variation can be observed not only at the mRNA transcript level but also at the (small) non-coding RNA (e.g., miRNA) transcript level, even when cells are genetically and morphologically identical. Moreover, each individual cell provides a unique microenvironment. miRNA expression profiling provides insights into cellular states (e.g., human cancer) and cellular mechanisms. miRNAs are known to regulate the expression of up to one-third of human genes, thus influencing a wide range of fundamental cellular functions. MiRNA profiling in single-cell or small cell pools is particularly important when observing highly variable expression across cells or when the number of available cells is very limited (e.g., early embryos). Furthermore, in the field of biomarker research, it is crucial to identify and analyze the expression of specific miRNA molecules in rare cell subpopulations that may be unidentifiable in complex cell pools. Analysis of miRNA levels in single-cell / small-cell pools involves five main steps: 1) isolation of individual cells, 2) cell lysis to release miRNAs that can be stabilized by lysis buffer, 4) polyadenylation and reverse transcription of miRNAs, and 5) quantification of miRNA levels (optionally pre-amplification of low-abundance miRNAs). Rapid and efficient extraction of miRNAs from single cells and stabilization after cell lysis are necessary to preserve the cell's natural "miRNome" and reduce output variations 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 variants, are the two main methods for single-cell RNA analysis to date. Similarly, qPCR and single-cell DNA sequencing (scDNA-seq) are the two main methods for single-cell genomic / mitochondrial DNA analysis.

[0009] Currently, quantitative real-time PCR (qPCR) is a widely used method for quantifying RNA and DNA targets. qPCR methods provide rapid and cost-effective analysis of one or more nucleic acid targets of interest; however, their target detection sensitivity and specificity depend heavily on the quantity and quality of the template, PCR conditions, and the presence of PCR inhibitors in the reaction. As reviewed 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, target DNA (which can also be reverse-transcribed RNA) is amplified in multiple cycles, where the number of target DNA molecules doubles (exponential amplification) at the end of each 100% effective PCR cycle. Therefore, theoretically, 2^n qPCR molecules can be generated after n cycles. n Each copy. However, in practice, PCR reagents will run out at some point, and the amplification products will anneal themselves, leading to a decrease in amplification efficiency until a plateau is reached and the amplification process saturates. At the end of a conventional PCR reaction, the amplification products can be analyzed using, for example but not limited to, agarose gel electrophoresis (endpoint measurement). The specificity of conventional PCR depends 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 uses fluorescence readings after each amplification cycle to measure the amount of amplified PCR product. A typical real-time PCR amplification plot shows an S-shaped curve (on a linear scale) and includes a baseline phase, followed by an exponential phase that plateaus through the linear phase. The exponential phase represents the most efficient amplification phase, and the amount of amplified PCR product doubles in each cycle (amplification efficiency is 100%). Real-time PCR enables relative quantification of the target and calibrators. The “absolute” amount of the target sequence in a qPCR reaction is measured relative to a standard curve generated from a sample of known quantity or copy number. This approach implies that the amplification efficiency of standards and samples is equivalent. Differences in PCR efficiency can significantly affect the accuracy of quantification (Quan et al., 2018, MDPI, “dPCR: A Technology Review”).

[0011] To quantify via qPCR, a standard curve is needed to calculate the amount of target nucleic acid in the sample. Typically, this standard curve is prepared by the user through serial dilutions. The preparation of the standard curve is crucial for accurate target quantification. Depending on the user's skill and the quality of the equipment used to prepare the standard curve, the cDNA or DNA dilution process and the preparation of the DNA standard curve itself can be very cumbersome, laborious, and error-prone, potentially leading to erroneous quantification results.

[0012] Compared to qPCR, single-cell sequencing provides high-throughput global RNA or DNA analysis from thousands of individual cells, but it may miss rare targets due to low sequencing coverage and high technical noise, especially with low starting material quantities. Additional difficulties arise when analyzing and interpreting large datasets obtained using scRNA-seq / scDNA-seq.

[0013] Another method for quantifying DNA (including reverse-transcribed RNA) is to use digital polymerase chain reaction (dPCR; also simply referred to as digital PCR, digital PCR or dePCR), which is an improvement on other polymerase chain reaction methods such as qPCR.

[0014] Digital polymerase chain reaction (dPCR) enables absolute quantification of target nucleic acids present in a sample, mitigating the limitations of qPCR. Unlike qPCR, dPCR does not rely on calibration curves for sample quantification. Therefore, it avoids the drawbacks associated with variations in reaction efficiency. dPCR is an absolute nucleic acid quantification method that relies on the detection of an endpoint fluorescence signal and the counting of a binary event (i.e., the presence or absence of fluorescence in a partition). In dPCR, the sample is first partitioned into many independent PCR sub-reactions, such that each partition contains several, one, or no target sequences. Several different methods can be used for sample partitioning, including microplates, microfluidic nanoplates, capillaries, oil emulsions such as water-in-oil droplets, and miniaturized chamber arrays with nucleic acid-binding surfaces.

[0015] Digital PCR (dPCR) offers several advantages over qPCR and scRNA-seq / scDNA-seq. In dPCR, the sample is distributed across thousands of nanopartitions, with each target molecule moving randomly into the partition. Unlike traditional PCR, which involves PCR reactions in large volumes of sample, this efficiently increases the effective concentration of each target molecule in the PCR reaction. Endpoint PCR is performed independently in each dPCR partition, tolerating potential contaminants or PCR inhibitors present in the sample. dPCR detects signals from individual partitions and uses Poisson statistics to calculate the absolute amount of target molecules, eliminating the need for a standard curve. Due to these characteristics, dPCR provides quantification of nucleic acid targets of interest with significantly higher sensitivity and specificity, independent of PCR efficiency. For example, multipath analysis of targets of interest allows focusing on specific targets rather than bulk transcriptomes or genomes. Furthermore, dPCR eliminates 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 budget-friendly manner, from isolation to target quantification. Therefore, the use of digital PCR provides unparalleled quantitative sensitivity and specificity for the analysis of nucleic acid expression or copy number in single cells or populations. It can be used to quantify subtle changes in the amount of rare targets at the single-cell level.

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

[0017] Digital PCR, when combined with high-accuracy single-cell sorting or cell separation instruments, solves two main problems: high detection sensitivity and accurate single-cell separation. This invention provides an efficient and high-throughput method for quantifying the amount of nucleic acid targets in well-defined individual cells or cell populations.

[0018] This invention provides a method for quantifying the amount of nucleic acid targets in well-defined individual cells or cell populations. The nucleic acid landscape of a single cell or cell population is captured at a specific time point, and the targets of interest are analyzed. Rapid transfer of cells to lysis buffer reduces artifacts because cells have less time to adapt to stressors or environmental changes. Furthermore, DNA and RNA loss is eliminated because there is no nucleic acid purification step. Instead, reverse transcription and digital PCR (or digital PCR only if RNA targets are not analyzed) are performed on the nucleic acids or targets of interest. Through the combination of accurate cell sorting / separation methods, rapid cell lysis, and plate-based digital PCR as a highly accurate quantification method, even low-abundance targets can be accurately quantified. The results can be compared with results from other (single) cells.

[0019] Therefore, the present invention provides a method for quantifying the amount of at least one nucleic acid target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; and b) Lyse at least one of the cells, thereby releasing RNA and DNA; and c) Reverse transcription of the released RNA to produce complementary DNA (cDNA), and further digital polymerase chain reaction (dPCR) of at least one reverse-transcribed RNA target within the cDNA; and / or Digital polymerase chain reaction (dPCR) is performed on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) Quantify the amount of at least one nucleic acid target. Detailed Implementation

[0020] This invention provides a method for quantifying the amount of at least one nucleic acid target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; and b) Lyse at least one of the cells, thereby releasing RNA and DNA; and c) Reverse transcription of the released RNA to produce complementary DNA (cDNA), and further digital polymerase chain reaction (dPCR) of at least one reverse-transcribed RNA target within the cDNA; and / or Digital polymerase chain reaction (dPCR) is performed on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) Quantify the amount of at least one nucleic acid target.

[0021] The present invention also provides a method for quantifying the amount of at least one ribonucleic acid (RNA) target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lysing the at least one cell to release RNA, wherein the released RNA contains at least one RNA target; c) Optionally, one or more DNA nucleases are used for a DNA digestion step to remove cellular DNA, wherein the one or more DNA nucleases are inactivated prior to a reverse transcription reaction; d) Reverse transcription is performed on the released RNA containing at least one RNA target to produce complementary DNA (cDNA). e) Perform digital polymerase chain reaction (dPCR) on at least one reverse-transcribed RNA target within the cDNA, wherein the dPCR is performed in a plate; f) Quantify the amount of at least one RNA target.

[0022] The present invention also provides a method for quantifying the amount of at least one coding RNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lysing the at least one cell to release RNA, wherein the released RNA contains coding RNA having at least one coding RNA target; c) Optionally, one or more DNA nucleases are used for a DNA digestion step to remove cellular DNA, wherein the one or more DNA nucleases are inactivated prior to a reverse transcription reaction; d) Reverse transcription is performed on the released RNA containing at least one coding RNA target to produce complementary DNA (cDNA). e) Perform digital polymerase chain reaction (dPCR) on at least one reverse-transcribed coding RNA target within the cDNA, wherein the dPCR is performed in a plate; f) Quantify the amount of at least one coding RNA target.

[0023] The present invention also provides a method for quantifying the amount of at least one non-coding RNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lysing the at least one cell to release RNA, wherein the released RNA contains non-coding RNA with at least one non-coding RNA target; c) Optionally, one or more DNA nucleases are used for a DNA digestion step to remove cellular DNA, wherein the one or more DNA nucleases are inactivated prior to a reverse transcription reaction; d) Reverse transcription is performed on the released RNA containing at least one noncoding RNA target to produce complementary DNA (cDNA). e) Perform digital polymerase chain reaction (dPCR) on at least one reverse-transcribed non-coding RNA target within the cDNA, wherein the dPCR is performed in a plate; f) Quantify the amount of the at least one non-coding RNA target.

[0024] The present invention also provides a method for quantifying the amount of at least one small non-coding RNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lysing the at least one cell to release RNA, wherein the released RNA contains a small non-coding RNA with at least one small non-coding RNA target; c) Optionally, one or more DNA nucleases are used for a DNA digestion step to remove cellular DNA, wherein the one or more DNA nucleases are inactivated prior to a reverse transcription reaction; d) Reverse transcription of the released RNA containing at least one small non-coding RNA target to produce complementary DNA (cDNA). e) Perform digital polymerase chain reaction (dPCR) on at least one small non-coding RNA target of reverse transcription within the cDNA, wherein the dPCR is performed in a plate; f) Quantify the amount of the at least one small non-coding RNA target.

[0025] The present invention also provides a method for quantifying the amount of at least one deoxyribonucleic acid (DNA) target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lyse at least one of the cells to release DNA; c) Perform digital polymerase chain reaction (dPCR) on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; d) Quantify the amount of the at least one DNA target.

[0026] The present invention also provides a method for quantifying the amount of at least one genomic DNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lyse at least one of the cells to release genomic DNA; c) Perform 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) Quantify the copy number of at least one genomic DNA target.

[0027] The present invention also provides a method for quantifying the amount of at least one mitochondrial DNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lyse at least one of the cells to release mitochondrial DNA; c) Perform 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) Quantify the amount of at least one mitochondrial DNA target.

[0028] The present invention also provides a method for quantifying the amounts of at least one genomic DNA target and at least one mitochondrial DNA target, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lyse at least one of the cells to release genomic DNA and mitochondrial DNA; c) Perform digital polymerase chain reaction (dPCR) on at least one genomic DNA target within the released genomic DNA and at least one mitochondrial DNA target within the released mitochondrial DNA, wherein the dPCR is performed in a plate; d) Quantify the amount of the at least one genomic DNA target and the at least one mitochondrial DNA target.

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

[0030] In another embodiment 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, the method comprising: a) Provide a specified number of cells, wherein the specified number of cells is at least one cell; b) Lyse at least one of the cells to release RNA and DNA; c) Reverse transcription of the released RNA to produce complementary DNA (cDNA), and further digital polymerase chain reaction (dPCR) of at least one reverse-transcribed RNA target within the cDNA, wherein the dPCR is performed in a plate; and / or Digital polymerase chain reaction (dPCR) is performed on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) Quantify the amount of at least one nucleic acid target.

[0032] In one implementation, steps b) and c) are performed on the same board.

[0033] In one embodiment, the reverse transcription reaction in step c) above is also performed on a plate (preferably the same plate used for subsequent dPCR).

[0034] In another aspect, the cells according to the invention are not placed in a lysis buffer, but are optionally loaded onto a plate and lysed by heating (thermal lysis). Thus, if nanoplates are used, a predetermined number of cells can be loaded into the wells. The intact cells are then partitioned into compartments and lysed by thermal lysis. The released nucleic acids can then be reverse transcribed (if desired) and subjected to dPCR.

[0035] Therefore, the present invention also provides a method for quantifying the amount of at least one nucleic acid target, the method comprising: a) A specified number of cells are provided on a plate, wherein the specified number of cells is at least one cell; b) Lysing at least one cell by thermal lysis, thereby releasing RNA and / or DNA; c) Reverse transcription of the released RNA to produce complementary DNA (cDNA), and further digital polymerase chain reaction (dPCR) of at least one reverse-transcribed RNA target within the cDNA, wherein the dPCR is performed in a plate; and / or Digital polymerase chain reaction (dPCR) is performed on at least one DNA target within the released DNA, wherein the dPCR is performed in a plate; and d) Quantify the amount of at least one nucleic acid target.

[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 dysregulation of expression of at least one coding RNA target or at least one non-coding RNA target or at least one genomic or mitochondrial structural variation (preferably copy number variation (CNV)). Using the quantitative method according to the invention, the amount of at least one nucleic acid target of interest can be determined. Since such an amount can be used as a prognostic biomarker known in the art in some embodiments, the risk of the subject developing cancer or disease can be assessed.

[0037] The present invention also provides an in vitro method for diagnosing a subject suffering from cancer or a disease, wherein the cancer or disease is associated with dysregulation of expression of at least one coding RNA target or at least one non-coding RNA target or at least one genomic or mitochondrial structural variation (preferably copy number variation (CNV)). Using the quantitative method according to the invention, the amount of at least one nucleic acid target of interest can be determined. Since such an amount is a biomarker of cancer or a specific disease in some embodiments and is known in the art, the subject can be diagnosed with cancer or said specific disease.

[0038] The present invention also provides an in vitro method for monitoring the progression of cancer or disease in subjects, wherein the cancer or disease is associated with dysregulation of expression of at least one coding RNA target, or with dysregulation of expression of at least one non-coding RNA target, or with at least one genomic or mitochondrial structural variation (preferably copy number variation (CNV)). Using the quantitative method according to the invention, the amount of at least one nucleic acid target of interest can be determined. Since such an amount can be used as a prognostic biomarker known in the art in some embodiments, the progression of cancer or disease in subjects can be monitored.

[0039] The present invention also provides an in vitro method for monitoring the response of a subject to a cancer or disease therapy, wherein the cancer or disease is associated with dysregulation of expression of at least one coding RNA target, or with dysregulation of expression of at least one non-coding RNA target, or with at least one genomic or mitochondrial structural variation (preferably copy number variation (CNV)). Using the quantitative method according to the invention, the amount of at least one nucleic acid target of interest can be determined. Since such an amount can be used as a prognostic biomarker known in the art in some embodiments, the subject's response to the cancer or disease therapy can be monitored.

[0040] In these areas, 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) disclosed several cancer-associated miRNAs and also the abundance of several miRNAs in different 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) reviewed miRNAs and their associations with different cancers and other diseases. This review article also elucidates the topic of miRNAs as disease biomarkers, including their roles in diagnosis and prognosis. 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) demonstrated that the miRNA expression profile of a muscle precursor cell subpopulation in humans is associated with type 2 diabetes. 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) disclosed the different mitochondrial DNA copy numbers in breast cancer tissue compared with 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) may have demonstrated 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) reported the presence of mitochondrial DNA copy number variations in various human cancers. Shlien and Malkin (“Copy number variations and cancer”, 2009, Genome Medicine, DOI: 10.1186 / gm62) reviewed 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) disclosed associations between specific CNVs and certain diseases, such as sensory-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. This disease is an autosomal dominant inherited disorder caused by the amplification of the CAG trinucleotide repeat in the Huntington 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) demonstrated that the expression levels of certain mRNAs or miRNAs in specific cancer types can serve as predictors of drug responses.

[0041] The present invention also provides kits for performing the methods according to the invention. Such kits may contain appropriate buffers, primers, probes, and other components required for performing the methods according to the 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 has or is suspected of having copy number variations (CNVs). CNVs are structural changes in the genome (e.g., deletions, insertions, duplications, translocations, and inversions) that result in copy number increases or losses ranging from hundreds of base pairs to entire chromosome regions. CNVs are either inherited or the result of changes in newborn somatic cells. CNVs contribute up to 10–20% of the variation in the genome and are a source of natural genetic diversity and biological dysfunction in humans. CNVs commonly lead to gene dysfunction, dosage imbalances, chromosome shortening, and position effects, and are associated with complex diseases and traits such as, but not limited to, cancer, obesity, aging, neurodegenerative diseases, and autoimmune diseases. Therefore, quantitative analysis of CNVs at disease-associated sites can provide insights into the molecular mechanisms of disease and has the potential to discover new biomarkers.

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

[0044] As used herein, "mitochondrial DNA target" generally refers to a region on mitochondrial DNA. In a preferred embodiment, "mitochondrial DNA target" refers to a region on mitochondrial DNA that has or is suspected of having copy number variations (CNVs). Similar to genomic CNVs, variations in mitochondrial DNA copy number (mtDNA CN) in blood or tissues have been associated with disease, which is not surprising given the important role of mitochondria in cellular homeostasis. In contrast to the fixed copy number of genomic DNA (the healthy diploid state), dramatic intercellular variations can be observed at the mitochondrial DNA (mtDNA) level, even among cells that are genetically and morphologically identical. mtDNA copy number fluctuates and varies between individuals and between different tissues, cells, and even within the same cell. Individual cells can carry hundreds to hundreds of thousands of mtDNA copies. However, dramatic variations in mtDNA copy number can lead to mitochondrial dysfunction and disease development. Therefore, similar to genomic CNVs, capturing mtDNA copy number heterogeneity is important for disease and drug development research and can be used as an indicator for screening metabolic diseases, cancer, prenatal testing, neurodegenerative diseases, and age-related diseases.

[0045] As used herein, "RNA target" refers to any type or subset of RNA analyzed / quantified by the method according to the present invention. Therefore, RNA targets include coding and non-coding RNA targets, etc. Prior to analysis, the RNA target needs to be reverse transcribed into cDNA, and then dPCR is performed on at least one reverse-transcribed RNA target within the cDNA.

[0046] As used in this article, “RNA-encoding target” refers to an RNA target that encodes a peptide / protein. Therefore, the term refers to messenger RNA (mRNA), such as, but not limited to, β-actin (ACTB) mRNA.

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

[0048] As used in this article, “non-coding RNA targets” refers to RNA targets that do not encode peptides / proteins. However, they may have specialized functions. According to Li and Chen (“Smalland Long Non-Coding RNAs: Novel Targets in Perspective Cancer Therapy”, 2015, Curr Genomics), these functions include, but are not limited to, regulation of transcription and translation or functions in, for example, protein scaffold formation. 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 intergenic non-coding RNAs (lincRNAs), natural antisense transcripts (NATs), highly conserved regions of transcription (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, the term “DNA target” as used herein refers to genomic and mitochondrial DNA targets. Depending on the context, this may mean it refers to both or one of them. For example, if the sentence is “dPCR against at least one DNA target (…),” then it means that if dPCR is performed against one DNA target, that target could be either a genomic or mitochondrial DNA target. However, if dPCR is performed against two DNA targets in this instance, then each of those two DNA targets can be independently selected from both genomic and mitochondrial DNA targets. The same principle applies to more than two DNA targets. The same general principle also applies to “RNA target.” If the term “nucleic acid target” (or simply “target”) is used herein, it means that each of these nucleic acid targets can be independently selected from the various DNA and RNA targets described herein.

[0050] Therefore, in one embodiment, each of the 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 of genomic DNA that has or is suspected of having copy number variations (CNVs), and the mitochondrial DNA target is a region of mitochondrial DNA that has or is suspected of having CNVs.

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

[0052] In one implementation, the genomic structural variation is a copy number variation.

[0053] In one implementation, the mitochondrial structural variation is a copy number variation.

[0054] In one implementation, 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 including one, two, three, or more targets. Similarly, as used herein, "at least two" means two, three, four, or more targets. For example, "at least two targets" means including two, three, four, or more targets.

[0056] As used in this article, the term "accuracy" refers to a measure of truth. Accuracy is how close a given set of measurements is to their true values. It is simply a description of systematic error, a measure of the statistical deviation of a given measure of central tendency. Low accuracy leads to discrepancies between the results and the true values.

[0057] "Accuracy" should not be confused with "precision," which defines how close measurements 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, a sample of a specified number of cells is provided, comprising eukaryotic, human, animal, plant, bacterial, archaea, oomycete, viral and / or fungal cells.

[0059] Preferably, the sample providing a specified number of cells is a eukaryotic sample. Most preferably, the sample providing a specified number of cells is a human sample.

[0060] In one embodiment, the sample providing a predetermined number of cells can be a male or female sample. Alternatively, the sample can contain a mixture of male and female cells. Thus, according to another embodiment, the sample contains one or more additional cells derived from different individuals.

[0061] Therefore, in one embodiment, the 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 cancer 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, a sample of a predetermined number of cells is provided from a subject who has cancer or is suspected of having cancer. In another embodiment, a sample of a predetermined number of cells is provided from a subject who has or is suspected of having a disease.

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

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

[0065] In some other embodiments, the cancer or disease is associated with at least one genomic or mitochondrial structural variation, preferably a copy number variation (CNV). The amount of the at least one genomic or mitochondrial structural variation can be quantified by the method according to the 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 and cell therapy, CAR-T therapy, intentional gene alterations induced by viral vectors or CRISPR modifications, organoid research, organelle quantification, and infectious disease research.

[0067] In one embodiment, the sample providing a specified number of cells may be derived from one of the following sample or tissue types: whole blood, blood fraction, plasma, serum, tumor cells, body fluids, oral samples, oral fluid, saliva, sputum, swabs, urine, human biological tissue, clothing samples containing biological material, vaginal swabs, sperm, skin or wound swabs, or other samples containing biological material or from other parts of the human body, as long as they can be used for nucleic acid isolation. As used herein, the terms “oral fluid” and “body fluid” refer to fluids excreted or secreted from the mouth and body, respectively, from which nucleic acids can be isolated. As a non-limiting example, oral fluid and body fluids may include saliva, sputum, swabs, and urine.

[0068] In one embodiment, a sample of a predetermined number of cells is provided, comprising cells, cell types, or cell lines, or mixtures of different (i.e., at least two) cell types, cell lines, or cell suspensions. In one embodiment, the cells, cell types, or cell lines include genetically modified cells, cell types, or cell lines. In one embodiment, the cells, cell types, or cell lines include unmodified cells, cell types, or cell lines. In one embodiment, the cells, cell types, or cell lines include both genetically modified cells, cell types, or cell lines and unmodified cells, cell types, or cell lines.

[0069] If necessary, prior to the step of providing a specified number of cells, the cells may be prepared from the sample by appropriate and applicable techniques known in the art, such as, but not limited to, trypsin digestion of cell culture cells (if applicable).

[0070] The precise number of cells can be obtained by various methods. In one embodiment, cells are separated or sorted, thus providing two, three, four, five, or more cells. In a preferred embodiment, a single cell is provided.

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

[0072] Fluorescence-activated cell sorting (FACS) is a technique commonly used to separate specific cells of interest (as single cells or as pools of cells of interest). The cells of interest are typically (but not necessarily) fluorescently labeled and flow through a flow cytometer. A laser excites the fluorophore. 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 separated based on their size and / or their light scattering properties.

[0073] Micromanipulation can be performed manually, i.e., by a professional technician using a microscope and micropipette to pick individual cells; or it can be performed mechanically, i.e., by using robots, etc., which are based on, for example but not limited to, visual input to pick individual cells.

[0074] Microfluidics enables the separation of cells of interest using small volumes of fluid and small amounts of sample. Cells can be separated based on labels or on inherent cell properties such as, but not limited to, size, shape, density, deformability, polarizability / impedance, and other hydrodynamic properties. For example, individual cells can be separated by traps, valves, or microdroplets, although other separation methods known in the art are also covered in the methods of this invention.

[0075] Immunoplasmation is used to separate cells based on the binding of cells to immobilized antibodies. Specifically, these antibodies bind to surface antigens on the cells of interest. Unbound cells are washed away, and the antibody-bound cells of interest can be recovered.

[0076] In magnetically activated cell sorting (MACS), cells of interest bind to antibodies, which in turn bind to magnetic beads. Specifically, these antibodies target antigens present on the cells of interest. A magnetic field is then applied, attracting the magnetic beads, while unbound cells flow away. The cells of interest bound to the beads can then be eluted and used in subsequent steps. Alternatively, the cells of interest may be those that do not bind to the beads, while unwanted cells are bound to them. In this case, the flowing-away cells need to be collected, while 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 cells of interest from tissue samples by applying a laser beam to cut a desired area previously marked with a microscope. This method can be used to obtain single cells or multiple cells.

[0078] In addition to the methods described above, similar cell separation methods known in the art are also included, capable of separating a precise number of cells, such as single cells, two cells, three cells, four cells, five cells, and more than five cells. One example is cellenONE. ® X1 technology. It is an image-based (bright field and / or 4-channel fluorescence) single-cell selection and separation method. Using this technology, samples containing only a few cells as small as 1 µl can be processed, and the resulting droplets can have a volume of 150-600 pL.

[0079] Preferably, the diameter, elongation parameters, and / or any targetable structures, such as but not limited to size, shape, particle size, protein or peptide, and / or nucleic acid, are known or can be received when performing the method of the invention. These parameters or structures can be used for the isolation of the one or more cells of interest.

[0080] In a preferred embodiment, at least one cell is directly isolated in a lysis buffer. Therefore, the RNA landscape of the cell is preserved as well as possible. Cells adapt their transcriptomes very rapidly to changes in the environment or stressors. Furthermore, ribonucleases present in the cell or environment can rapidly degrade RNA. Therefore, RNA is relatively unstable, and the faster the cells of interest are lysed, the fewer artifacts or changes in the RNA landscape, which is an important requirement for single-cell RNA analysis. In principle, all types of RNA present in the cells can be analyzed in the corresponding method of the present invention, such as, but not limited to, mRNA and / or miRNA. In one embodiment, cell lysis and all subsequent steps of the method of the present invention, namely reverse transcription (if RNA is being analyzed) and dPCR, are performed on the same plate. The advantage of using a single plate for all steps from lysis to dPCR is that no material is lost during pipetting and purification steps, and the procedure is time-efficient due to the need for fewer pipetting steps. Furthermore, the risk of contamination during operation is minimized. Additionally, the cells being analyzed are intact before being loaded onto the plate, so if loaded onto a nanoplate, their nucleic acid contents will be distributed across the partitions of the nanoplate.

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

[0082] As used herein, “lysing at least one cell to release RNA” means at least the release of RNA. The release of other types of nucleic acids (e.g., genomic DNA or mitochondrial DNA) is optional if they do not contain a target of interest.

[0083] As used herein, “lysing at least one cell to release DNA” means at least the release of genomic and / or mitochondrial DNA. The release of other types of nucleic acids (e.g., RNA) is optional if they do not contain a target of interest.

[0084] As used herein, "lysing at least one cell to release mitochondrial DNA" means at least the release of mitochondrial DNA. The release of other types of nucleic acids (e.g., genomic DNA or RNA) is optional if they do not contain a target of interest.

[0085] In cases where direct isolation to the lysis buffer may not be possible, such as, but not limited to, requirements dependent on the isolation or sorting apparatus or the kit used, cells may be isolated into phosphate-buffered saline or other buffer solutions or into a suitable culture medium, followed by rapid lysis. In a preferred embodiment, the lysed cells are used directly for subsequent steps, such as for reverse transcription and dPCR reactions, or for dPCR reactions if RNA is not analyzed. However, the method according to the invention also allows for the storage of the lysates prior to performing subsequent steps according to the method. Thus, in another embodiment, the lysates are stored, for example, at about 4°C, -20°C, or -80°C, until they are used in subsequent steps of the method according to the invention.

[0086] In a preferred embodiment of the method according to the invention, the lysis buffer contains a component that stabilizes RNA and / or DNA. In an alternative embodiment, the RNA and / or DNA stabilizing component may be provided in the solution containing cells to be sorted or separated, or directly in the final cell solution used prior to lysis. In another alternative embodiment, the RNA and / or DNA stabilizing component may be provided shortly 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) preparing RNA lysates directly from cells using one or more buffers, ii) stabilizing cellular RNA, and optionally iii) removing contaminating DNA. These kits eliminate the need for an RNA purification step and accelerate and simplify the preparation of RNA samples from cells in a high-throughput and reproducible manner. They also positively impact the accuracy of the present invention. Therefore, the method according to the present invention involving RNA target analysis does not require an additional RNA extraction step after cell lysis. Instead, the lysate or its fractions can be used directly in subsequent steps. Since no additional RNA purification step is required, this further speeds up the procedure and avoids the loss of RNA, particularly low-abundance RNA that might otherwise be lost in additional purification steps.

[0088] Similarly, commercial kits can be used to i) prepare lysates directly from cells using one or more buffers, and optionally ii) stabilize cellular RNA and, optionally, DNA. These kits eliminate the need for RNA and / or DNA purification steps and accelerate and simplify sample preparation from cells in a high-throughput and reproducible manner. They also have a positive impact on the accuracy of the invention. Therefore, the method according to the invention does not require additional RNA and / or DNA extraction steps after cell lysis. Instead, the lysates or fractions thereof can be used directly for subsequent steps. Since no additional RNA and / or DNA purification steps are required, this further speeds up the procedure and avoids the loss of nucleic acids, especially low-abundance nucleic acid targets that might otherwise be lost in additional purification steps.

[0089] In a preferred embodiment of the method for DNA target analysis according to the invention, the lysis buffer does not contain a DNA nuclease, 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, if RNA targets are being analyzed but not DNA targets, it is preferable to add one or more DNA nucleases to eliminate the risk of detecting genomic DNA instead of complementary DNA (cDNA) in subsequent steps of the method according to the invention. Standard DNA nuclease concentrations known in the art can be used. The one or more DNA nucleases are then inactivated before the reverse transcription step. Inactivation can be performed by using a DNA nuclease inhibitor, or by using a DNA nuclease-resistant aptamer targeting the DNA nuclease, or by chelating ions required for DNA nuclease activity, or by RNA purification, or by DNA nuclease precipitation. Preferably, DNA nuclease inactivation is performed by heat inactivation, such as, but not limited to, incubation at 75°C for 5 minutes.

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

[0091] The lysis buffer may also contain a protease, such as proteinase K. If proteinase K is present in the lysate, the sample is heat-inactivated before subsequent steps. Heat inactivation protocols for proteinase K are known in the art; a non-limiting example is incubation at 75°C for 5–10 min.

[0092] Therefore, in one embodiment, the pyrolyte is thermally inactivated before subsequent steps.

[0093] In another embodiment, the pyrolyte is not thermally inactivated prior to subsequent steps.

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

[0095] Using the lysate directly in subsequent steps of the method according to the invention without prior purification of DNA and / or RNA has the advantage of not losing nucleic acids during purification. Therefore, using the method according to the invention, even targets with very low abundance (e.g., RNA targets present as low as 1 copy per cell) can be accurately detected and their absolute quantity quantified.

[0096] In one embodiment, lysis is performed in a reaction vessel. In an alternative embodiment, lysis is performed in a plate, different from the plate in which reverse transcription is performed. Optionally or additionally, lysis is performed in a plate, different from the plate in which dPCR is performed. In another embodiment, lysis is performed in the same plate in which reverse transcription (if performed) and dPCR are performed.

[0097] In one embodiment, for dPCR or for dPCR performed directly after reverse transcription, prior to the (reverse transcription and) amplification steps, a dPCR reaction mixture or an RT-dPCR reaction mixture containing the lysate or its fractions is dispensed into multiple partitions, and (reverse transcription and) dPCR is performed in each of the multiple partitions.

[0098] In a preferred embodiment of the method according to the invention relating to RNA target analysis, reverse transcription is performed directly on the RNA released from the lysate or on fractions of the RNA released from the lysate (e.g., for a target of interest). Specifically, reverse transcription is performed on the released RNA containing at least one RNA target. In one embodiment, random primers, target-specific primers, or oligomeric (dT) primers may 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 in which dPCR is later performed. In another embodiment, the reverse transcription reaction is performed in the same plate in which dPCR is later performed. The cDNA or its fractions are used as templates for subsequent dPCR reactions.

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

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

[0101] In one embodiment, the dPCR is performed on at least one reverse-transcribed RNA target within the cDNA. In this embodiment, the 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 can be independently selected from the group comprising low-copy, medium-copy, and high-copy targets. Alternatively, each of the at least one target can be independently selected from the group consisting of low-copy, medium-copy, and high-copy targets.

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

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

[0104] In one embodiment, the 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 targets in the method according to the invention is independently selected from the group consisting of low-copy targets, medium-copy targets and high-copy targets, or, in the case of more than one target, from the group consisting of multiple low-copy targets, medium-copy targets and high-copy targets.

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

[0107] In addition to lysing one or more cells of interest, the RT-dPCR reaction mixture (for RNA target analysis or RNA and DNA target analysis) or dPCR reaction mixture (for DNA target analysis) contains buffers and dNTPs in addition to the required enzymes.

[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 as labeled derivatives, such as those containing fluorescent, radiolabeled, or biotin-labeled derivatives. dNTPs with modified bases are also included, such as hypoxanthine, xanthine, 7-methylguanine, inosine, xanthinosine, 7-methylguanosine, 5,6-dihydrouracil, 5-methylcytosine, pseudouridine, dihydrouridine, or 5-methylcytosine. Furthermore, ddNTPs of the above molecules are also covered in this invention.

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

[0110] Detection of amplification products may include direct or indirect fluorescent and / or chemical labeling of amplicones and / or labeling based on polynucleotides and / or peptides or proteins using a composition comprising one or more of the following: probes and / or guide molecules and / or proteins and / or peptides and / or nucleic acids and / or derivatives of the components listed above. In one embodiment, the probes and / or amplification products may be labeled with one or more fluorophores, quenchers, barcodes, indexes, peptides, proteins, radiolabels, biotinylate tags, and / or chemically attached to their components. In another embodiment or further, the probes and / or amplification products may contain modifications, such as base modifications, sugar modifications, and / or backbone 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, an embedding dye or other known DNA marker or detection agent can be used instead of a probe.

[0112] Preferably, quantification is based on detecting at least one amplified nucleic acid target by using at least one probe for each analyzed target, wherein the at least one probe specifically binds to each amplification product of each nucleic acid target. Therefore, different targets require different probes. Preferably, the at least one probe is fluorescently labeled.

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

[0114] In one implementation, quantification is based on detecting 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 analyzed, wherein the at least one probe specifically binds to each amplification product of each target.

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

[0116] Besides cell isolation methods and lysate generation methods, the techniques used for nucleic acid quantification greatly affect the accuracy and robustness of the results.

[0117] Following PCR, the concentration of the target nucleic acid was quantified with statistically defined accuracy using Poisson statistics, where partitions with 0, 1, or more target sequences were used individually for computational purposes. Interestingly, the sample partitions efficiently concentrated the target sequences within isolated microreactors. This concentration effect reduced template competition, thus enabling the detection of rare mutations in a wild-type sequence background (Quan et al., 2018, MDPI, “dPCR: A Technology Review”).

[0118] dPCR also allows for greater tolerance to inhibitors present in the sample because it does not require nearly 100% amplification efficiency per cycle as is required for qPCR. Instead, it is sufficient if a signal is detected or not detected at the end of the amplification reaction.

[0119] Similar to normal PCR, dPCR also involves a single reaction within the sample; however, the sample is divided into numerous partitions, and the reaction is performed individually in each partition. This separation allows for more reliable collection and sensitive measurement of nucleic acid levels.

[0120] dPCR involves dividing a PCR solution into at least hundreds, but in most cases thousands or tens of thousands of nanoliter-sized partitions, where a separate PCR reaction occurs in each partition. The preparation of dPCR solutions is similar to that of quantitative assays, using probes (e.g., fluorescence quencher probes) or intercalation dyes, and a PCR master mixture containing optimal concentrations of DNA polymerase, dNTPs, MgCl2, and reaction buffer.

[0121] After multiple PCR amplification cycles, the fluorescence of the sample is checked using binary readings of "0" (absent) or "1" (present). The fraction of the fluorescent partitions is recorded. The partitioning of the sample allows one to estimate the number of different molecules by assuming that the molecular population follows a Poisson distribution, thus taking into account the possibility that multiple target molecules may be present in a single partition.

[0122] Unlike qPCR, dPCR is an endpoint PCR reaction. dPCR uses the number of fluorescently positive regions in the total sample to inversely calculate the target concentration. Unlike qPCR, sample quantification in dPCR does not require a calibration curve. In summary, compared to qPCR, dPCR provides more robust quantification, is less susceptible to inhibitors, and is not dependent on quantitative standards.

[0123] The benefits of dPCR include improved precision through large sample partitioning, which ensures reliable measurement of the desired target due to reproducibility. Furthermore, dPCR is highly quantitative because it does not rely on the relative fluorescence of the solution to determine the amount of target nucleic acid amplified.

[0124] According to the present invention, dPCR is performed in a plate. It offers many advantages over other dPCR methods, including those using microfluidic chips and disks, microarrays, microdroplets, or oil-water emulsion-based droplet crystals.

[0125] In contrast to the plate-based dPCR of this invention, droplet digital PCR (ddPCR) is a dPCR method in which a sample reaction of, for example, 20 μL (containing assay primers and TaqMan probes or intercalating dyes) is divided into approximately 20,000 nanoliter droplets using an oil-water emulsion technique, thermally cycled to the endpoint in a 96-well PCR plate, and the fluorescence amplitude of all droplets is read in a droplet flow cytometer. Each droplet needs to be measured individually.

[0126] In contrast, plate-based dPCR enables faster readout because all partitions of the well (sample) can be read simultaneously. Other key advantages of dPCR in plates (e.g., but not limited to nanoplates) include, but are not limited to: i) user-friendly, familiar plates are easy to pipette, just like qPCR, which also facilitates users with little or no experience using microfluidics; ii) fixed partitions prevent size variations and aggregation (which can occur in droplet-based dPCR), thereby maximizing the consistency of partition volume and number; iii) sealed (nano) plates prevent well-to-well contamination; iv) integrated quality control exists, meaning that one can view the plate image to obtain a single fluorescence signal from the counts of each positive partition, so the user receives not only a table with counts but also an image; v) plates are suitable for front-end automation, minimizing hands-on steps; vi) larger volumes can be loaded into the plate compared to droplet dPCR; vii) greater scalability in terms of loading volume and the number of partitions and wells.

[0127] Furthermore, the time before results are published in droplet digital PCR is typically longer than in plate-based digital PCR. Additionally, the workflow and operation of droplet dPCR are more complex.

[0128] Furthermore, the chemistry used for droplet dPCR differs from that used in plate-based dPCR. Droplet dPCR requires an oil-water emulsion to form droplets, and the mixture must contain reagents to stabilize the droplets so they do not disperse after formation. Additionally, the mixture cannot contain reagents that interfere with the droplets. Plate-based dPCR does not require these specific chemical requirements; it uses a mechanical partitioning method instead of a chemical one.

[0129] One consequence of these features is that plate-based dPCR offers unprecedented technical efficiency, providing greater accuracy and precision compared to droplet-based dPCR because each plate in which dPCR is performed provides identical conditions (e.g., the same number, size, and volume of partitions) for each analysis and each sample. This differs in this respect from droplets, which are unstable and variable in size, distorting the distribution of target molecules; see also Ko... Ir 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] In addition, plate-based PCR offers the advantage of achieving higher and faster throughput because all partitions of the well can be read simultaneously.

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

[0132] According to the research of 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”), dPCR fits a Poisson distribution based on two assumptions: all partitions are of equal volume, and target molecules are randomly distributed among the partitions. In practice, this means that each partition has an equal chance of containing a target molecule. The number of target molecules present in a positive partition can be one, two, or more molecules, and it is currently impossible to determine how many molecules a given positive partition might contain. However, the number of molecules in a negative partition is known. If all partitions are of equal volume, the average concentration (λ) of target molecules in each partition can be estimated from the probability that a partition is negative, using the proportion of negative partitions and the Poisson distribution. This concentration is derived from the number of negative partitions (w) and the total number of partitions (n) in the reaction:

[0133] Quantification is not possible when all partitions contain the amplification target, meaning all partitions have positive signals and the dPCR experiment is saturated.

[0134] Therefore, for exemplary dPCR systems using 8500, 26000, or 22000 partitions per well, the upper limit of the dynamic range is theoretically reached when 8499 (= 99.98%), 25999 (= 99.99%), or 21999 (= 99.99%) partitions each contain at least one target molecule, i.e., a reverse-transcribed RNA molecule from an RNA target. Therefore, for the above dPCR systems, λ will be (rounded to the nearest integer):

[0135] The λ value mentioned above marks the statistical / theoretical limit of the dPCR method.

[0136] The minimum number of molecules required per analytical input sample volume for a positive partition in dPCR is 1 (= the lowest detectable target concentration in the dPCR experiment).

[0137] In one embodiment, the quantitative result of the at least one target is compared with the quantitative result of the at least one target in at least another cell. According to the method of the invention, the at least another cell also undergoes the same steps as one or more cells being compared with the at least another cell. The at least another cell may be derived from the same sample or another sample. The at least another cell may be the same or different cell types. The at least another cell may have the same or different characteristics.

[0138] In one implementation, the quantification result of the at least one target is compared with the quantification result of the at least one target within the cDNA and / or released DNA of at least another cell. Thus, insights can be obtained regarding 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 specific manner. In all these cases, the readings may not be compensated for by many other cells present in an undefined cell pool.

[0139] Typically, the "plate" according to the invention is a microtiter plate known in the art. In one embodiment, the plate according to the invention is a nanoplate. Nanoplates are microtiter plates. Different plates can be used for different steps of the method, particularly for lysis, reverse transcription, and dPCR steps. Non-limiting examples include 96-well plates, 384-well plates, or nanoplates.

[0140] In one embodiment, the plate according to the invention has at least 2 holes, at least 6 holes, at least 8 holes, at least 12 holes, at least 24 holes, at least 48 holes, at least 96 holes, at least 192 holes, at least 384 holes, at least 768 holes, at least 1536 holes, 6 to 1536 holes, 24 to 1536 holes, 96 to 1536 holes, 192 to 1536 holes Holes, 384 to 1536 holes, 12 to 384 holes, 12 to 96 holes, 12 to 48 holes, 24 to 384 holes, 24 to 96 holes, 24 to 48 holes, 8 to 96 holes, 2 to 384 holes, 2 holes, 6 holes, 8 holes, 12 holes, 24 holes, 48 ​​holes, 96 holes, 192 holes, 384 holes, 768 holes, or 1536 holes.

[0141] In one embodiment, the plate according to the invention has 0 partitions / holes, at least 10 partitions / holes, at least 100 partitions / holes, at least 500 partitions / holes, at least 1000 partitions / holes, at least 2000 partitions / holes, at least 3000 partitions / holes, at least 4000 partitions / holes, at least 5000 partitions / holes, at least 6000 partitions / holes, at least 7000 partitions / holes, at least 8000 partitions / holes, at least 8500 partitions / holes, at least 9000 partitions / holes, at least 10000 partitions / holes, at least 12000 partitions / holes, at least 14000 partitions / holes, at least 15000 partitions / holes, at least... 18,000 partitions / holes, at least 20,000 partitions / holes, at least 22,000 partitions / holes, at least 25,000 partitions / holes, at least 26,000 partitions / holes, at least 30,000 partitions / holes, at least 50,000 partitions / holes, 10 to 26,000 partitions / holes, 100 to 26,000 partitions / holes, 4,000 to 26,000 partitions / holes, 8,000 to 50,000 partitions / holes, 8,000 to 30,000 partitions / holes, 8,000 to 26,000 partitions / holes, 8,000 to 25,000 partitions / holes, 8,000 to 22,000 partitions / holes, 8,000 to 20,000 partitions / holes, 8 000 to 18000 partitions / holes, 8000 to 15000 partitions / holes, 8000 to 14000 partitions / holes, 8000 to 12000 partitions / holes, 10000 to 50000 partitions / holes, 10000 to 30000 partitions / holes, 10000 to 26000 partitions / holes, 10000 to 25000 partitions / holes, 10000 to 22000 partitions / holes, 10000 to 20000 partitions / holes, 10000 to 18000 partitions / holes, 10000 to 15000 partitions / holes, 10000 to 14000 partitions / holes, 10 partitions / holes, 100 partitions / holes Holes, 500 partitions / hole, 1000 partitions / hole, 2000 partitions / hole, 3000 partitions / hole, 4000 partitions / hole, 5000 partitions / hole, 6000 partitions / hole, 7000 partitions / hole, 8000 partitions / hole, 8500 partitions / hole, 9000 partitions / hole, 10000 partitions / hole, 12000 partitions / hole, 14000 partitions / hole, 15000 partitions / hole, 18000 partitions / hole, 20000 partitions / hole, 22000 partitions / hole, 25000 partitions / hole, 26000 partitions / hole, 30000 partitions / hole, or 50000 partitions / hole.

[0142] Increasing the number of partitions will improve sensitivity.

[0143] Any combination of the number of holes and the number of partitions / holes disclosed above is covered within the scope of this invention.

[0144] Typically, the plates used for dPCR are microtiter plates.

[0145] In one embodiment, the plate in which dPCR is performed has at least 2 wells, at least 6 wells, at least 8 wells, at least 12 wells, at least 24 wells, at least 48 wells, at least 96 wells, at least 192 wells, at least 384 wells, at least 768 wells, at least 1536 wells, 6 to 1536 wells, 24 to 1536 wells, 96 to 1536 wells, 192 to 1536 wells, and so on. 36 holes, 384 to 1536 holes, 12 to 384 holes, 12 to 96 holes, 12 to 48 holes, 24 to 384 holes, 24 to 96 holes, 24 to 48 holes, 8 to 96 holes, 2 to 384 holes, 2 holes, 6 holes, 8 holes, 12 holes, 24 holes, 48 ​​holes, 96 holes, 192 holes, 384 holes, 768 holes, or 1536 holes.

[0146] In one embodiment, the plate in which dPCR is performed has at least 10 partitions / wells, at least 100 partitions / wells, at least 500 partitions / wells, at least 1000 partitions / wells, at least 2000 partitions / wells, at least 3000 partitions / wells, at least 4000 partitions / wells, at least 5000 partitions / wells, at least 6000 partitions / wells, at least 7000 partitions / wells, at least 8000 partitions / wells, at least 8500 partitions / wells, at least 9000 partitions / wells, at least 10000 partitions / wells, at least 12000 partitions / wells, at least 14000 partitions / wells, at least 15000 partitions / wells, and at least 18 partitions / wells. 000 partitions / holes, at least 20000 partitions / holes, at least 22000 partitions / holes, at least 25000 partitions / holes, at least 26000 partitions / holes, at least 30000 partitions / holes, at least 50000 partitions / holes, 10 to 26000 partitions / holes, 100 to 26000 partitions / holes, 4000 to 26000 partitions / holes, 8000 to 50000 partitions / holes, 8000 to 30000 partitions / holes, 8000 to 26000 partitions / holes, 8000 to 25000 partitions / holes, 8000 to 22000 partitions / holes, 8000 to 20000 partitions / holes, 800 0 to 18,000 partitions / holes, 8,000 to 15,000 partitions / holes, 8,000 to 14,000 partitions / holes, 8,000 to 12,000 partitions / holes, 10,000 to 50,000 partitions / holes, 10,000 to 30,000 partitions / holes, 10,000 to 26,000 partitions / holes, 10,000 to 25,000 partitions / holes, 10,000 to 22,000 partitions / holes, 10,000 to 20,000 partitions / holes, 10,000 to 18,000 partitions / holes, 10,000 to 15,000 partitions / holes, 10,000 to 14,000 partitions / holes, 10 partitions / holes, 100 partitions / holes 500 partitions / holes, 1000 partitions / holes, 2000 partitions / holes, 3000 partitions / holes, 4000 partitions / holes, 5000 partitions / holes, 6000 partitions / holes, 7000 partitions / holes, 8000 partitions / holes, 8500 partitions / holes, 9000 partitions / holes, 10000 partitions / holes, 12000 partitions / holes, 14000 partitions / holes, 15000 partitions / holes, 18000 partitions / holes, 20000 partitions / holes, 22000 partitions / holes, 25000 partitions / holes, 26000 partitions / holes, 30000 partitions / holes, or 50000 partitions / holes.

[0147] Increasing the number of partitions will improve sensitivity.

[0148] Any combination of the number of holes and the number of partitions / holes disclosed above is covered within the scope of this invention.

[0149] The choice of plate (including the number of wells, partitions / wells, and loading volume) can be particularly dependent on the application, required throughput, and sample availability. For rare events, plates with more partitions / wells (e.g., 26,000 partitions / wells) may be beneficial. Similarly, for targets with highly diluted samples, plates with more partitions / wells (e.g., 26,000 partitions / wells) may also be beneficial. If high throughput is desired or required, plates with more wells but potentially fewer partitions / wells may be better, such as a 96-well plate with 8,500 partitions / wells.

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

[0151] The nanoplates are either 24-well or 96-well plates, meaning that up to 24 or 96 different samples can be loaded, respectively. A 24-well nanoplate has approximately 8,500 or 26,000 partitions / well. A 96-well nanoplate has approximately 8,500 partitions / well. Typically, the sample input volume for a nanoplate with approximately 26,000 partitions / well is 40 µl, and for a nanoplate with approximately 8,500 partitions / well, it is 12 µl.

[0152] For a given number of partitions / wells, there is little flexibility in terms of reaction volume. If the volume is too small, the reaction may be infeasible; if the volume is too large, it may, for example, affect downstream initiation. The volume of the circulating sample needs to be precisely determined to quantify the amount of at least one nucleic acid target in dPCR. Generally, plates in which dPCR is performed provide partitions of a fixed size, enabling accurate and reproducible concentration calculations. However, to compensate for even minimal volume variations between different wells of the plate or between different batches of plates, a compensation factor can be used for concentration calculations.

[0153] When using QIAcuity ® In digital PCR systems, one example of such a compensating factor is the volumetric precision factor (VPF) available per plate. The VPF is a set of factors per well. It consists of 96 individual factors that address variability between wells and reduce variations between different mold forms that cause batch-to-batch variability. This improves the precision of concentration measurements in dPCR, particularly for sensitive applications such as the analysis of rare nucleic acid targets.

[0154] To achieve higher accuracy, multiple wells can be grouped and analyzed as a single well (metawell). For analysis purposes, a metawell can be treated as a single well but with more partitions. This can be helpful for rare event detection if the volume of the sample to be analyzed exceeds the volume that can be loaded into a single well. Alternatively or additionally, wells with more partitions can be used for rare event detection.

[0155] Further details of the results obtained by the method according to the invention will be described in detail in the illustrations and embodiments.

[0156] Example

[0157] The following examples are used to illustrate the present invention. The sites amplified and detected should not be considered as limiting the scope of the present invention, but are merely exemplary sites that can be used within the scope of the present invention.

[0158] Example 1: Workflow for analyzing RNA-coding targets (mRNA)

[0159] The general workflow of this embodiment is shown in Figure 1 In the middle. CellenONE ® A cell sorting system was used to directly sort cells into lysis buffer. Reverse transcription and subsequent digital PCR were performed in the plate to quantify at least one mRNA target. Detailed experimental procedures are described below.

[0160] Cell culture, resuspension, and sample preparation: Two days prior to isolation, HEK293 and HeLa cells were passaged and cultured under standard conditions (DMEM / F12, containing 10% FBS and penicillin, streptomycin, amphotericin B, 37°C, 5% CO2). Before isolation, cells were washed twice with PBS, detached from their culture plates (0.5 ml trypsin for 1 min at 37°C), centrifuged (250 xg for 5 min at 4°C), and resuspended in PBS (400 cells / μl). The cell suspension was stored on ice and diluted to 200 cells / μl in degassed PBS just before processing.

[0161] Plate preparation: In cellenONE ® The X1 software defines different 384-well microtiter plate (MTP) layouts, with each well containing a specific number of cells (ranging from 1 to 100 cells per well). For two cell samples, cell diameter and elongation parameters are defined to precisely separate a specific number of single cells in each well.

[0162] The 384-well MTP plates were pre-loaded with 10 μl / well of Fast Lane one-step cell lysis buffer (available as part of the Fast Lane Cell Probe Kit, catalog number 216413) and stored on ice until processing. The pre-loaded plates were then transferred to cellenONE. ® On the target scaffold (pre-cooled to 4°C), for (single) cell detection and isolation.

[0163] Instrument configuration and cell separation: Prior to separation, small aliquots of the two cell line samples were processed to determine the optimal separation parameters for each cell line (see [link to sample 1]). Figure 2A After configuration, according to such Figure 2B The defined design layout shown uses cellenONE. ® The X1 system isolated HEK293 and HeLa cells into three 384-well MTP wells.

[0164] Successful cell isolation: Using cellenONE ® The technology successfully isolated cells, including single cells. Figure 3A An example of isolated HEK293 cells is shown. Once all cells were isolated, a cellenREPORT was generated for each 384-well MTP, which compiles all parameters and images for each isolated cell. The diameter and elongation parameters of the isolated cells were within specified ranges (see [link to relevant documentation]). Figure 3B (and 3C).

[0165] Direct cell lysis and single-cell gene expression analysis were performed on the QIAcuiti digital PCR system (Qiagen, catalog number 911021) using the FastLane Cell Probe Kit (Qiagen, catalog number 216413) and the QIAcuiti OneStep Advanced Probe PCR Kit (Qiagen, catalog number 250131).

[0166] Production of cell lysates: Cellular RNA lysates were prepared using the Fast Lane One-Step Cell Buffer Kit (available as part of the Fast Lane 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. In addition to lysing cells, this buffer stabilizes cellular RNA and genomic DNA, eliminating the need for RNA purification. Cells were directly isolated into 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 for 5 minutes on a heating block (QINSTRUMENTSColdPlate Slim version, catalog number 2016-0111). The plate was then frozen at -80°C and stored until further processing.

[0167] Reverse transcription digital PCR (RT-dPCR): The reaction was set up according to the standard QIAcuity OneStep Advanced Probe Kit rapid start protocol. For each condition, 2 μl of cell lysate was added directly to the RT-dPCR reaction mixture. The total volume of the reaction mixture was 12 µl. Multiplexing of target gene expression levels was performed using a TaqMan probe-based assay. The RT-dPCR reaction mixture was then pipetted into the wells of a QIAcuity 8.5k nanoplate. The nanoplate was sealed and placed in the QIAcuity Digital PCR Instrument (Series 0; R&D version) according to the instrument's user manual. The standard QIAcuity One-Step RT-dPCR cycle program was selected. The results were analyzed using the QIAcuity software suite (Supply Suite 2.0.20, CSW 2.0.0.144).

[0168] High-sensitivity detection of absolute transcript copy numbers at the single-cell level: use Figure 1 The workflow outlined in the document analyzes the transcript levels of various targets in HEK293 and HeLa cells. For example... Figure 4A As shown in HEK293 cells and 4B (HeLa cells), differences in target gene expression levels across different cell lines can be easily studied in a high-throughput manner. Cell lysis following cell sorting allows for the accurate number of cells used in RT-dPCR reactions. High-quality cell sorting, lysate preparation, and RT-dPCR enable optimal cell analysis over a wide linear range. Figure 4A And 4B). Furthermore, multiple targets can be analyzed simultaneously (and 4B). Figure 5It is worth noting that, due to the low detection limit of dPCR, transcripts with low intracellular abundance can still be accurately detected. Because of partitioning and endpoint PCR of lysates, dPCR can sensitively detect the absolute copy number of transcripts at the single-cell level. Figure 6 The method according to the invention can also be used to study the variability of target gene expression levels from one individual cell to another in an absolute, high-throughput manner. Figure 7A -C).

[0169] Therefore, the workflow of this invention achieves high-throughput absolute quantification of low-abundance targets at the single-cell level. Real-time and 100% accurate single-cell isolation without compromising viability and transcript expression allows for RT-dPCR reactions using an accurate number of intact cells. Furthermore, the elimination of the RNA purification step significantly reduces practical operation time. Additionally, it can be multiplexed in a one-step RT-dPCR format.

[0170] In summary, this simple yet efficient workflow, which combines the isolation of a specified number of cells with absolute gene expression analysis, provides highly sensitive, reproducible, and linear quantification of transcript levels in cell lysates.

[0171] Example 2: Workflow for DNA (Genomic DNA and / or mtDNA) Analysis

[0172] The general workflow of this embodiment is shown in Figure 8 In the middle. CellenONE ® A cell sorting system was used to directly sort cells into lysis buffer that did not contain DNAseI. Digital PCR was performed in the plate, and CNV or mitochondrial copy numbers were analyzed. Detailed experimental procedures are described below.

[0173] Cell culture, resuspension, and sample preparation: Two days prior to isolation, HEK293 and HeLa cells were passaged and cultured under standard conditions (DMEM / F12, containing 10% FBS and penicillin, streptomycin, amphotericin B, 37°C, 5% CO2). Before isolation, cells were washed twice with PBS, detached from their culture plates (0.5 mL trypsin at 37°C for 1 min), centrifuged (250 g at 4°C for 5 min), and resuspended in PBS (400 cells / μL). The cell suspension was stored on ice and diluted to 200 cells / μL in degassed PBS just before processing.

[0174] Plate preparation: The 384-well MTP target was pre-loaded with 3 μl / well of Fast Lane one-step cell lysis buffer (available as part of the Fast Lane Cell Probe Kit, catalog number 216413; DNase I-free) and stored on ice until processing. The pre-loaded plates were then transferred to cellenONE. ® On the target scaffold (pre-cooled to 4°C), for (single) cell detection and isolation.

[0175] Board layout: like Figure 9 As shown, before cell sorting, in cellenONE ® Different layouts were designed and set in the X1 software. One 384 MTP layout was designed with 384 single cells, one cell per well, and a second 384 MTP layout was designed with a different number of cells per well (ranging from 1 to 100 cells per well).

[0176] Instrument configuration and single-cell isolation: Prior to separation, small aliquots of different cell samples were processed to determine the optimal separation parameters for each cell line, as shown in Table 1:

[0177] For both cell samples (HEK293 and HeLa cells), only cell diameter and elongation parameters were defined to precisely separate a specific number of cells into each well. After configuration, according to... Figure 9 The layout defined in [the document] uses cellenONE. ® The X1 system separated HEK293 and HeLa cells into three 384 MTP wells. Actual parameters of the sorted cells are shown in Table 2:

[0178] Cells were directly isolated into Fast-Lane lysis buffer (see below).

[0179] Direct cell lysis and single-cell PCR were performed using the QIAcuity probe PCR kit on the QIAcuity digital PCR system. Cellular target copy number analysis: Production of cell lysates: DNA lysates were prepared from cells using Fast Lane cell lysis buffer without DNase I. Cells were directly isolated into the wells of a 384-well MTP plate containing 3 µl of Fast Lane lysis buffer (DNase I-free). The plates were then incubated at ambient temperature for 5 minutes, followed by heating to 75°C for 5 minutes (QInstrument ColdPlate Slim; 2016-0111) for heat inactivation of the lysis buffer. Subsequently, the plates were frozen at -80°C until further processing.

[0180] Successful isolation of a single cell: Figure 10A An example of isolated HeLa cells is shown. Once all cells were isolated, a cellenREPORT was generated for each 384MTP, which compiled all parameters and images for each isolated cell. The diameter and elongation parameters of the isolated single cells were within specified ranges (Table 2 and...). Figure 10B ).

[0181] Digital PCR: The dPCR reaction was set up according to the standard QIAcuity dPCR probe CNV assay quick start protocol (HB-3060). For each condition, the total amount (3 µl) of cell lysate containing various cell volumes was directly added to the dPCR reaction mixture. The QIAcuity dPCR probe CNV assay and the TaqMan probe-based assay were used for multiplexing of target copy number levels at the gDNA and mtDNA levels, respectively. The dPCR reaction mixture was then pipetted into the wells of a QIAcuity 8.5k or 26k nanoplate. The nanoplate was sealed and placed in the QIAcuity digital PCR instrument (Series 0) according to the instrument's user manual. The standard QIAcuity dPCR probe CNV assay cycle program was selected. The results were analyzed using the QIAcuity software suite (suites 2.0.20 and 2.1.7.187).

[0182] Copy number variation of genomic DNA (gDNA) and mitochondrial DNA (mtDNA) at both single-cell and population levels. High-sensitivity analysis: Use the workflow outlined above (see also...) Figure 8This method is used to examine the copy numbers of various genomic and mtDNA targets in HEK293 or HeLa cells. Cell sorting prior to cell lysis provides the accurate number of cells loaded into the dPCR reaction. The high accuracy of cell sorting, efficient lysate preparation, and highly sensitive dPCR detection allow for optimal copy number analysis in both single cells and cell populations. According to the method of the present invention, multiple targets in a cell can be analyzed simultaneously in a single reaction. A major challenge associated with multiplexed copy number analysis is that different targets may have different copy numbers. Although many gDNA targets are present in two copies in a healthy diploid genome, mtDNA targets may have much higher copy numbers in the same cell than gDNA targets. This simultaneous detection of variable copy number ranges requires not only high sensitivity but also a high dynamic detection range. Due to partitioning and endpoint PCR of the lysate, dPCR provides sensitive detection of the absolute copy numbers of both low-copy and high-copy targets at high resolution at the single-cell level. Figure 11A And B).

[0183] Furthermore, the high dynamic range of the method according to the invention allows for accurate readout of copy number changes in high-copy targets as cell loading density increases. Figure 12 Therefore, in a single multiplexing reaction, copy number variations of various targets with low, medium, and high copy numbers can be detected with high accuracy and reproducibility. Furthermore, using the method according to the invention, copy number differences of mtDNA or gDNA targets in different cell types can be analyzed in a high-throughput manner. Figure 13 In addition to studying cell populations, this workflow can also be used to examine single-cell heterogeneity of gDNA and mtDNA copy numbers in an absolute, high-throughput manner. Figure 14 (This demonstrates the single-cell heterogeneity of mtDNA).

[0184] In summary, this simple yet efficient workflow, which combines cell sorting with dPCR and copy number analysis, provides highly sensitive, reproducible, and linear quantification of genomic or mitochondrial DNA target copy numbers in cell lysates in a high-throughput manner.

[0185] The method according to the 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 dPCR reactions to use an accurate number of intact cells. Furthermore, the use of lysis buffers that stabilize RNA and DNA and eliminate the need for purification of nucleic acids of interest significantly reduces practical operation time. Moreover, the method according to the invention allows for target multiplexing in a single dPCR reaction with little or no optimization. In summary, this simple yet efficient workflow combining cell sorting with dPCR and copy number analysis provides highly sensitive, reproducible, and linear quantification of target genomic or mitochondrial copy numbers in cell lysates.

[0186] Example 3: Workflow for analyzing small non-coding RNA targets (e.g., miRNAs)

[0187] The general workflow of this embodiment is shown in Figure 15 In the middle. CellenONE ® A cell sorting system was used to directly sort cells into lysis buffer. Reverse transcription and digital PCR were performed in the plate, and target miRNAs were analyzed and quantified. Detailed experimental procedures are described below.

[0188] Cell culture, resuspension, and sample preparation: Immortalized HEK293 cells were passaged two days prior to isolation and cultured under standard conditions (DMEM / F12, containing 10% FBS and penicillin, streptomycin, amphotericin B, 37°C, 5% CO2). Prior to isolation, cells were washed twice with PBS, detached from the culture plate (0.5 ml trypsin at 37°C for 1 min), centrifuged (250 x g at 4°C for 5 min), and resuspended in PBS (400 cells / μL). The cell suspension was stored on ice and diluted to 200 cells / μL in degassed PBS just before processing.

[0189] Plate preparation: Using the FastLane Cell Probe Kit, mix 47.6 µl of buffer FCPL with 2.4 µl of gDNA Wipeout Buffer 2. Target 384-well MTP is pre-filled with 10 μl / well of this Fast Lane one-step cell lysis buffer (available as part of the FastLane Cell Probe Kit, catalog number 216413) and stored on ice until processing. The pre-filled plate is then transferred to cellenONE. ® On the target scaffold (pre-cooled to 4°C), for single-cell detection and isolation.

[0190] Cell isolation: In cellenONE® In X1 software, a specific 384 MTP layout is prepared, which has a specified number of cells ranging from 300 cells per well to 1 cell per well. Figure 16 Prior to separation, small aliquots of cells were processed to determine the optimal separation parameters for HEK293 cells. Only cell diameter and elongation parameters were used to precisely separate a specific number of cells into each well. Optimal parameters: minimum diameter 16.03 μm, maximum diameter 30 μm. 1.60 μm was used as the maximum elongation parameter. After configuration, according to... Figure 16 The layout defined in [the document] uses cellenONE. ® The X1 system isolated HEK293 cells into wells of 384 MTP.

[0191] Direct cell lysis and miRNA detection were performed using the QIAcuity EG PCR kit on the QIAcuity dPCR system. analyze: Production of cell lysates: Cellular RNA lysates were prepared using the Fast Lane One-Step Cell Buffer Kit (available as part of the Fast Lane Cell Probe Kit, catalog number 216413). In addition to lysing cells, the buffer 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 for 5 minutes on a heating block. The plate was then frozen at -80°C and stored until further processing.

[0192] miRNA polyadenylation and reverse transcription were performed using the miRCURY LNA RT kit: Cell lysates were used directly as input for the miRCURY LNA RT reaction. One reaction mixture consisted of 10 μL cell lysates, 4 μL 5x miRCURY RT SYBR Green reaction buffer, 2 μL miRCURY enzyme mixture, and 4 μL water. The reaction mixture was thoroughly mixed and incubated at 42 °C for 60 min. Polyadenylation and reverse transcription of the miRNA occurred in this step. The reaction was then incubated at 95 °C for 5 min. The cDNA was stored at -20 °C until further use.

[0193] dPCR for miRNA quantification: Before setting up the dPCR reaction, the cDNA was diluted 7.5-fold in water. The QIAcuity EGPCR mixture was used in the dPCR. The master mixture, along with the miRCURY LNA miRNA PCR assays hsa-miR-10a-5p and hsa-miR-10b-5p, were used at a final concentration of 1x. No additional water was added to the final reaction (template content was 66% in the dPCR reaction). The reaction mixture was prepared in pre-plates and then transferred to 8.5k or 26k nanoplates. The plates were sealed and placed in the QIAcuity dPCR instrument according to the instrument's user manual. The miRCURY dPCR standard cycle was selected according to the manual. The results were analyzed using the QIAcuity software suite (suite 2.1.8).

[0194] High-sensitivity detection of absolute miRNA copy number at the single-cell level: The expression levels of hsa-miR-10a-5p and hsa-miR-10b-5p targets were analyzed in HEK293 cells. Figure 17 and Figure 18A As shown in 18B and 18C, both miRNAs can be detected with high linearity, ranging from cell pools containing 300 cells to single-cell levels. Therefore, the method according to the invention allows for high-throughput analysis of miRNAs derived from different cell lines and for analysis of different cell pool sizes.

[0195] The rapid, real-time, and 100% accurate single-cell isolation method according to the present invention enables RT and dPCR reactions using an accurate number of intact cells. High-quality cell isolation, lysate preparation, RT, and dPCR allow for optimal cell analysis over a wide linear range. Figure 17 (and 18A, 18B, and 18C). Due to the low detection limit of dPCR, even low-abundance miRNAs in cells can be accurately detected. Furthermore, due to partitioning and endpoint PCR of the lysate, dPCR can sensitively detect the absolute copy number of miRNAs, even at the single-cell level. The method according to the invention can also be used to study the variability of miRNA levels in individual cells in an absolute, high-throughput manner. In addition, the elimination of the RNA purification step significantly reduces practical operation time.

[0196] In summary, this simple yet efficient workflow, which combines accurate cell isolation in lysis buffer with absolute miRNA quantification, provides highly sensitive, reproducible, and linear quantification from cell lysates used as input for RT and dPCR.

[0197] Example 4: Analytical target workflow, where cell lysis and dPCR are performed in the same plate.

[0198] To further improve the time and cost efficiency of the method of the present invention, intact cells can also be directly loaded onto a plate containing lysis buffer, which is later used for dPCR. An exemplary workflow is described below.

[0199] 10 µl of CaseWorks Go lysis buffer (Qiagen, material number: 1116186) was preloaded into 24-well 26kd PCR plates. 2 µl of the prepared cell suspension was loaded into the preloaded lysis buffer (PBS, 10 µl per well). 4 -1 cell). Seal the plate with Airpore foil (Qiagen, material number: 1017662) and incubate at room temperature for 10 min.

[0200] Optionally, i.e., if proteinase K is present in the lysate, the sample is heat-inactivated after the aforementioned steps, for example at 75°C for 5 min. When proteinase K is present, the plate can be sealed, for example, with Airpore foil (Qiagen, material number: 1017662) and incubated, for example, in an incubator. The sheet will prevent a) contamination and b) reduced evaporation. Condensation will not be on the foil, unlike qPCR covering foil which can also be used.

[0201] The plate was then unsealed, and 28 µl of the previously prepared master mixture (see, for example, Table 3 below) was added and mixed with the lysate by pipetting up and down (avoiding bubble formation). The plate was then sealed with a nanoplate sealer (Qiagen) and dPCR was run. Exemplary cycling and imaging conditions are shown in Tables 4 and 5 below, respectively.

[0202] Table 3: MM settings suitable for a 24-well 26k dPCR plate

[0203] Table 4: dPCR cycling

[0204] Table 5: dPCR Imaging

[0205] Three different experiments were conducted using the general workflow described above. They are described in more detail in Sections 4.1–4.3 below.

[0206] Example 4.1: Using 1 to 100,000 cells

[0207] For this experiment, the following dPCR layout was used:

[0208] The main mixture is set as follows:

[0209] The plan is as follows:

[0210] The dPCR parameters are as follows:

[0211] Example 4.2: Using 0.58 to 580 cells

[0212] For this experiment, the following dPCR layout was used:

[0213] The main mixture is set as follows:

[0214] The plan is as follows:

[0215] The dPCR parameters are as follows:

[0216] Example 4.3: Using 0.9 to 900 cells

[0217] For this experiment, the following dPCR layout was used:

[0218] The main mixture is set as follows:

[0219] The plan is as follows:

[0220] The dPCR parameters are as follows:

[0221] Figure 19 depicts Example 4.1 ( Figure 19A ), 4.2 ( Figure 19B ) and 4.3 ( Figure 19C A quantitative table of the experimental results.

[0222] Figure 20 depicts Example 4.1 ( Figure 20A ), 4.2 ( Figure 20B ) and 4.3 ( Figure 20C A scatter plot of the experimental results.

[0223] Figure 21 It is a graph depicting the experimental results of all examples 4.1-4.3. Attached Figure Description

[0224] Figure 1 A simplified workflow for (single-cell) gene expression analysis.

[0225] Figure 2: Single-cell isolation parameters and plate layout used for single-cell isolation of example cell lines HeLa and HEK293.

[0226] A) Optimal (single) cell isolation parameters for HeLa and HEK293 cells.

[0227] B) Using cellenONE ® Schematic diagrams of three different layouts for cell separation using the X1 system. (A) 384-well MTP layout for separating a single HeLa cell per well; (B) 384-well MTP layout for separating a single HEK293 cell per well; (C) 384-well MTP layout for separating different numbers of HeLa and HEK293 cells per well.

[0228] Figure 3: Using cellenONE ® Single cells isolated using X1 technology.

[0229] Image A) shows a single HEK293 cell within the capillary expulsion zone before separation. The pore coordinates (A1) and parameters (D: diameter (22.77 µm), E: elongation (1.35 µm), I: gray intensity (70.51)) of the separated cell are shown at the top of the image. To the right of the cell, two vertical lines are shown in the image. They "delineate" the deposition zone, which is a safe zone. If this zone contains at least one additional cell, the cell in the expulsion zone (represented here as the area to the left of the deposition zone) will be discarded.

[0230] B) By cellenONE ® The HEK293 and HeLa separation scatter plots generated by the X1 analysis module show the relationship between cell elongation and cell diameter. All separated cells are represented by a single point with the darkest gray shading.

[0231] C) Parameters of isolated (single) cells.

[0232] Figure 4: Analysis of gene expression levels in different cell lines at different cell densities. Results for HEK293 and HeLa cell lines were obtained using the ACTB-FAM assay in RT-dPCR.

[0233] A) Analysis of gene expression levels in HEK293 cells. Left: Each dot represents a positive region; depicting the fluorescence intensity of the positive region in each well for different numbers of analyzed cells. Right: A graph depicting the relationship between the target concentration (shown as copies / µl) and the number of analyzed cells.

[0234] B) Analysis of gene expression levels in HeLa cells. Left: Each dot represents a positive region; depicting the fluorescence intensity of the positive region in each well for different numbers of analyzed cells. Right: A graph depicting the relationship between the target concentration (shown as copies / µl) and the number of analyzed cells.

[0235] Figure 5 Simultaneous quantification of low, medium, and high abundance targets in cell lysates during multiplexed RT-dPCR reactions. HeLa cell lysates were loaded with cell densities gradually increasing. Multiplexing was performed using ACTB-FAM, MYC-ROX, and KDR-HEX assays. Each dot represents a positive partition. Fluorescence intensity of the positive partitions in each well was depicted for different numbers of analyte cells.

[0236] Figure 6High-sensitivity analysis of gene expression levels in single cells. Target ACTB transcripts (C and D, respectively) were detected in individual HEK293 cells (depicted as dots) using the ACTB-FAM assay in one-step RT-dPCR. Transcripts were not detected in the non-template controls (NTC) (A and B). Positive regions are highlighted here with white arrows pointing to fluorescent regions. Each subplot represents a well with 8500 regions.

[0237] Figure 7: Multipath analysis of gene expression levels in single cells. Single-cell (HeLa) lysates and non-template controls (NTC; the last two columns of the last row for each plate) were loaded onto 8.5k nanoplates and tested using ACTB-FAM, MYC-ROX, and CDK2NA-HEX assays.

[0238] A) ACTB-FAM determination method.

[0239] B) MYC-ROX assay.

[0240] C) CDK2NA-HEX assay.

[0241] Figure 8 : A simplified workflow for (single-cell) genome CNV and / or mitochondrial copy number analysis.

[0242] Figure 9 via cellenONE ® Schematic diagrams of two plate layouts for cell separation using the X1 system. Left: 384-MTP layout for separating a single cell per well. Right: 384-MTP layout for separating different numbers of cells per well (0, 1, 2, 5, 10, 25, 50, or 100 cells per well), showing magnified views of wells A1-8, B1-8, C1-8, D1-8, E1-8, F1-8, G1-8, and H1-8.

[0243] Figure 10: Using cellenONE ® Single-cell isolation using X1 technology.

[0244] Image A) shows a single HeLa cell within the capillary expulsion zone before separation. The pore coordinates (2) and parameters (D: diameter (25.79 μm), E: elongation (1.54), I: gray intensity (71.50)) of the separated cell are shown at the top of the image. To the right of the cell, two vertical lines are shown in the image. They "delineate" the deposition zone, which is a safe zone. Cells in the expulsion zone are discarded if this zone contains at least one additional cell.

[0245] B) By cellenONE ®An example of a scatter plot generated by the X1 analysis module, showing the relationship between cell elongation and cell diameter. All separated cells are represented by a single point with the darkest gray shading. Left: HeLa separation scatter plot. Right: HEK293 separation scatter plot.

[0246] Figure 11: Genomic DNA (gDNA) and mitochondrial DNA (mtDNA) targets show different copy numbers at the single-cell level. Copy number differences can be visualized at high resolution using 1D scatter plots or signal maps in the QIAcuity software suite. Results were obtained using A) ND4-FAM, RPP30-HEX, and SPIN4-ROX assays and B) CYB-FAM, RP-30-HEX, and SPIN4-ROX assays in a single multiplexed digital PCR reaction loaded into an 8.5k nanoplate. The SPIN4 (Chr. X) and RPP30 (Chr. 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.

[0247] A) 1D scatter plot, where points above the horizontal threshold line correspond to regions in a single HEK293 cell that showed a positive fluorescent signal after amplification of the indicated target. Well numbers are shown at the top.

[0248] B) Signal plot showing the positive regions of the assay indicated in a single HEK293 cell. Positive regions are highlighted here using white arrows pointing to the fluorescent regions.

[0249] Figure 12 Simultaneous quantification of low, medium, and high copy number targets in HEK293 cell lysates. Cell lysates were loaded into the reaction at gradually increasing cell densities. Three replicates from each loading condition are shown. Results were obtained using TERT-ROX, AMY1A-FAM, and ND4-Cy5 assays in a multiplexed digital PCR reaction loaded into 26k nanoplates. TERT (Chr. 5, low copy number) and AMY1A (Chr. 11, medium copy number) are genomic DNA targets, while ND4 (high copy number) is a mitochondrial DNA target.

[0250] Figure 13The copy number detection of genomic DNA and mitochondrial DNA targets was linear with increasing HEK293 or HeLa cell numbers per reaction. AMY1A-FAM, TERT-ROX, and ND4-Cy5 assays were used for multiplexed digital PCR reactions loaded onto 26k nanoplates. AMY1A (Chr. 11, medium copy number) and TERT (Chr. 5, low copy number) were genomic DNA targets, while ND4 (high copy number) was a mitochondrial DNA target. Results are expressed as mean copies / µl obtained from three replicates. R under all conditions 2 > 0.97.

[0251] Figure 14 Copy number analysis of mitochondrial DNA using targeted CYB and ND4 assays revealed heterogeneity in mitochondrial DNA copy number in single cells. Single-cell lysates (row A: HEK293, row B: HeLa) were loaded onto 8.5k nanoplates and tested using either CYB-FAM or NB4-FAM assays.

[0252] Figure 15 A simplified workflow for (single-cell) miRNA analysis.

[0253] Figure 16 Using cellenONE ® The X1 system is used for plate layouts for cell separation. Cells are separated into pools containing 300 cells / well, 150 cells / well, 75 cells / well, 35 cells / well, 5 cells / well, 2 cells / well, or 1 cell / well. Magnified views of wells A1-4, B1-4, C1-4, D1-4, E1-4, F1-4, G1-4, H1-4, and I1-4 are shown.

[0254] Figure 17 Quantitative analysis of miRNAs in different numbers of sorted cells. Cells were sorted and lysed at different densities, and miRNAs were reverse transcribed and quantified by dPCR on 26k nanoplates. Quantification was performed using a miRCURY LNA miRNA PCR assay targeting hsa-miR-10b-5p.

[0255] Figure 18: High-sensitivity analysis of miRNAs across a wide range of HEK293 cell infusions.

[0256] A) miRNA expression was highly linear across the range from 300 sorted cells to single cells. The miRCURY LNA miRNA PCR assays for hsa-miR-10a-5p and hsa-miR-10b-5p were used. For each target, the quantified copies / µl were plotted against the cell number.

[0257] B) miRNA expression was highly linear across the range from 300 sorted cells to single cells. The miRCURY LNA miRNA PCR assay hsa-miR-10b-5p was used. For each target, the quantified copies / µl were plotted against the cell number.

[0258] C) Signal plots for different cell numbers, showing positive regions indicating different cell numbers (fluorescent spots in the image). The miRCURY LNA miRNA PCR assay (hsa-miR-10b-5p) was used.

[0259] Figure 19: Quantitative results of genomic DNA under extensive Jurkat and HeLa cell infusion.

[0260] A) Quantitative results of the experiments outlined in Example 4.1. 1–100,000 (1:10 dilution) Jurkat cells were analyzed in triplicate, and the amounts of MYC and RPP30 copies / µl were quantified.

[0261] B) Quantitative results of the experiments outlined in Example 4.2. Triple copies of 0.58–580 (1:10 dilution) HeLa cells were analyzed to quantify the levels of MYC and RPP30 copies / µl.

[0262] C) Quantitative results of the experiments outlined in Example 4.3. Triple copies of 0.9–900 (1:10 dilution) HeLa cells were analyzed to quantify the levels of MYC and RPP30 copies / µl.

[0263] Figure 20: A scatter plot showing the fluorescence intensity measured for genomic DNA targets in Jurkat cell lysates and HeLa cell lysates.

[0264] A) Jurkat cell lysates with gradually decreasing cell density were loaded into the reaction. Three parallel samples from each loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiments are described in Example 4.1.

[0265] B) HeLa cell lysates with gradually decreasing cell density were loaded into the reaction. Three parallel samples from each loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiments are described in Example 4.2.

[0266] C) HeLa cell lysates with gradually decreasing cell density were loaded into the reaction. Three parallel samples from each loading condition are shown. Results were obtained using MYC-FAM and RPP30-HEX assays in digital PCR reactions loaded into 26k nanoplates. The experiments are described in Example 4.3.

[0267] Figure 21 Figure 19 depicts the quantitative results of all the experiments described in Examples 4.1-4.3.

Claims

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

2. The method of claim 1, wherein at least one cell provided is directly isolated into a lysis buffer.

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

4. The method of claim 3, wherein the coding RNA target is mRNA, the non-coding RNA target is miRNA, the genomic DNA target is a region of genomic DNA that has or is suspected of having copy number variation (CNV), and the mitochondrial DNA target is a region of mitochondrial DNA that has or is suspected of having CNV.

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

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

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

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

9. The method according to any one of the preceding claims, wherein quantification is based on detecting at least one amplified nucleic acid target by using at least one probe on each analyzed target, wherein the at least one probe specifically binds to each amplification product of each nucleic acid target.

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

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

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

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

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

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

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