Bacterial Single Cell Whole Transcriptome Amplification in Microfluidic

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Cite This: Anal. Chem. 2019, 91, 8036−8044

Bacterial Single Cell Whole Transcriptome Amplification in Microfluidic Platform Shows Putative Gene Expression Heterogeneity Yuguang Liu,†,‡ Patricio Jeraldo,†,‡ Jin Sung Jang,∥ Bruce Eckloff,∥ Jin Jen,∥ and Marina Walther-Antonio*,†,‡,§

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Department of Surgery, Division of Surgical Research, ‡Microbiome Program, Center for Individualized Medicine, §Department of Obstetrics and Gynecology, and ∥Medical Genome Facility, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States S Supporting Information *

ABSTRACT: Single cell RNA sequencing is a technology that provides the capability of analyzing the transcriptome of a single cell from a population. So far, single cell RNA sequencing has been focused mostly on human cells due to the larger starting amount of RNA template for subsequent amplification. One of the major challenges of applying single cell RNA sequencing to microbial cells is to amplify the femtograms of the RNA template to obtain sufficient material for downstream sequencing with minimal contamination. To achieve this goal, efforts have been focused on multiround RNA amplification, but would introduce additional contamination and bias. In this work, we for the first time coupled a microfluidic platform with multiple displacement amplification technology to perform single cell whole transcriptome amplification and sequencing of Porphyromonas somerae, a microbe of interest in endometrial cancer, as a proof-of-concept demonstration of using single cell RNA sequencing tool to unveil gene expression heterogeneity in single microbial cells. Our results show that the bacterial single-cell gene expression regulation is distinct across different cells, supporting widespread heterogeneity.

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based on reverse transcription (RT) to generate template complementary DNA (cDNA) and amplify the cDNA by φ29 polymerase and random primers. The MDA method is reported to have high fidelity and lower error rates following isothermal procedures that are easy to implement in microfluidic systems.22−24 So far, most of the SC-WTA applications have been focused on human cells.25,26 SC-WTA of bacterial species is still rare due to the rigid and multilayered cell walls of these microorganisms27 and their 100−500× lower amount of starting total RNA than single eukaryotic cells for total transcriptome amplification.19,28,29 To generate sufficient cDNA from the miniscule amount of RNA from a single cell for downstream sequencing and analyses, multiple rounds of amplification30,31 are often adopted. However, the additional processes would take up to 48 h and can increase the percentage of duplicated reads and thus bias in the data analyses. To extend microfluidic-based SC-WTA to microbiological and microbiome research, it is essential to develop

henotypically identical and genetically similar cells from the same population can have dramatic heterogeneity in their behavior, which is often expressed in their transcriptomic landscape.1 This heterogeneity plays a significant role in various biological processes, including tumor progression2,3 and immune response.4 Single cell whole transcriptome sequencing (SC-WTS) is emerging as a powerful tool for profiling the cell-to-cell variability to uncover this heterogeneity, which has been obscured in bulk studies.1,5−7 As a result, SC-WTS is starting to impact our understanding of human physiology and diseases such as how particular cells can be the determinants of an infection,8 drug resistance,9,10 and cancer relapse.11 The key steps in SC-WTS include single cell isolation, lysis, and amplification of femto to picograms of total RNA to reach the quantity sufficient for library preparation and sequencing. Microfluidic platforms are ideal for single cell applications, as they offer the unique ability of handling nanoliters of fluid in a controlled manner, thus, allowing for the manipulation and isolation of single cells for subsequent nanoscale reactions12−17 with minimal contamination and marginal reagent cost. Multiple displacement amplification (MDA)18 has been a popular method for whole genome19,20 and transcriptome amplification due to the relatively low amplification bias.21 It is © 2019 American Chemical Society

Received: October 17, 2018 Accepted: June 3, 2019 Published: June 3, 2019 8036

DOI: 10.1021/acs.analchem.8b04773 Anal. Chem. 2019, 91, 8036−8044

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Analytical Chemistry

Figure 1. Overview of the workflow of bacterial SC-WTA for sequencing. The process includes cell culture and RNA protection treatment, single cell isolation, RT, and cDNA amplification in microfluidic chip. Blue rectangles standard off-chip processes. Yellow box highlights the on-chip steps.

chamber and incubated for 10 min. The bacterial cells were then pelleted at 12000 g for 3 min at 4 °C, and the supernatant was aspirated. The cell pellet was washed twice with nucleasefree water and resuspended in 1 mL of nuclease-free water, then the sample was divided into two 0.5 mL aliquots. RNA was extracted from one aliquot using RNeasy Micro Kit (Qiagen) following manufacturer’s instruction, and was stored at −20 °C for bulk RNA sequencing without amplification in the microfluidic device. Pluronic F-127 (10%, Sigma) was added to the other aliquot (final concentration of 0.08%) before the cells were introduced into the microfluidic device for single cell isolation and whole transcriptome amplification. Microfluidic Experimental Setup. The study was performed in our optofluidic platform at Mayo Clinic (Rochester, MN) with minor modifications of the microfluidic device used in our previous work.33,34 Briefly, this platform integrates a microscope (Nikon Eclipse), optical tweezers (Thorlabs), and a customized polydimethylsiloxane (PDMS) microfluidic device with 12 parallel reaction lines with sets of valves that allow for the on-demand creation of isolated microenvironments. The number of chambers in each reaction line corresponds to the number of reagents that needs to be sequentially added to perform the reactions. We chose this microfluidic structure as it has been widely used and validated in a number of single cell genomic works that rely on optical

protocols suitable for a single bacterial cell that circumvent the aforementioned concerns. In this work, we demonstrate an MDA-based bacterial SCWTA in a microfluidic platform that produces 2−3 ng of cDNA for downstream library preparation and sequencing. In this proof-of-concept study, we used Porphyromonas somerae as the object because this bacterium is not considered as a common contaminant; moreover, this species was recently identified as one of the microbiome signatures of gynecological diseases, including endometrial cancer, and thus, has the potential to be used as a biomarker for early disease detection.32 The results revealed heterogeneity of gene expression in single cells that can be obscured by bulk cell gene expression studies. We believe that an effective microfluidic-based protocol for bacterial SC-WTA would serve as a guideline for bacterial single cell transcriptomic analyses in microfluidic platforms and can be applied to a wide range of biological research and medical applications.

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MATERIALS AND METHODS Cell Preparation. P. somerae (ATCC, BAA1230) was cultured in chopped meat carbohydrate broth (DB) at 37 °C in an anaerobic chamber (Coy) and harvested during log phase (∼107/mL). Two volumes of RNAprotect Bacteria Reagent (Qiagen) was added to the culture inside the anaerobic 8037

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Figure 2. On-chip single cell RNA amplification. (a) Comparison of on-chip amplification of 100 and 0.1 pg of purified P. somerae total RNA and a single P. somerae cell. (b) Comparison of amplification yield vs amplification time. N = 10 per experiment, error bars show standard deviation.

tweezers for single cell isolation.35−37 The major advantages of using optical tweezers to isolate single cells from a population include high target single cell confidence, providing a way to visually ensure that only one cell is trapped into a microchamber and, thus, maintains a minimal possibility of sequencing contaminating cells unintentionally. Prior to the experiments, the sample channel in the microfluidic device was presoaked in the chip diluent (0.04% Pluronic F127 in phosphate buffered saline (PBS)) for 30 min to prevent the cells from sticking to the PDMS surface during cell sorting. The cell sample was introduced into the chip, and single cells were trapped and transported into microchambers by optical tweezers. Visually identifiable contaminating cells were trapped and moved out of the chambers to ensure only the target cell was in the chamber prior to the lysis step. Reagents, including lysis buffers, RT mix, ligation mix, and polymerase, were sequentially added to the isolated single cells to perform chemical reactions in the same way as described in our recent work.33 The amplified products were collected from the outlet ports of the device and transferred into microwell plates for downstream processing. All the supplies and reagents were filtered (0.2 μm), autoclaved, or UV-sterilized, except for the RT mix, ligation mix, and polymerase. A total of 10 single cell reactions and two negative control (sterile PBS) reactions were performed in the on-chip SC-WTA experiment. Lysis Buffer Components. The on-chip bacterial SCWTA was performed using primarily REPLI-g WTA Single Cell Kit (Qiagen), except for a customized lysis buffer, as the lysis buffer provided in the kit does not support the lysis of bacterial or plant cells. Our lysis buffer was adapted from a recipe reported by Kang et al.,30 which includes 100 mM TrisCl (pH 8), 200 mM KCl, 0.5 mM ethylenediaminetetraacetic acid (EDTA), 0.1% Triton X-100, 200 mM dithiothreitol (DTT; BioRad), 0.04 U/μL RnaseOut (Invitrogen), and 3 × 10−7 U/μL Ready-Lyse lysozyme (Epicenter). The rest of the SC-WTA reagents were prepared using supplied components according to the kit manufacturer’s instruction with the addition of a low concentration of Tween 20. A detailed description of these reagents is provided in Supporting Information. Microfluidic Bacterial Lysis for SC-WGA Workflow. The general workflow of SC-WTA in a microfluidic chip is shown in Figure 1. After single cell isolation in the microfluidic chip using laser tweezers, customized lysis buffer was introduced into the chambers and incubated on a hot plate at 37 °C for 15 min, followed by incubation at 80 °C for 2 min

for RNA denaturation. Genomic DNA (gDNA) wipeout buffer was then added and incubated at 42 °C for 10 min to remove gDNA released from the cell. RT mix was added and incubated at 42 °C for 1 h to transcribe the bacterial RNA into a firststrand cDNA template, and this reaction was terminated by incubating at 95 °C for 3 min. Ligation buffer was then added and incubated at 24 °C for 30 min to create a second-strand cDNA, synthesizing the double-stranded cDNA ready for MDA-based amplification. The ligation process was terminated by incubation at 95 °C for 5 min. φ29 polymerase was added and incubated at 30 °C for 2 h, followed by the inactivation of all enzymes at 65 °C for 5 min. Gel-loading pipet tips were inserted into the outlet ports of the chip, and nuclease-free water with 0.02% Tween 20 (Sigma) was introduced into the chip to flush the amplified into the pipet tips until the fluid level reached the 20 μL mark. The product was collected and stored at 4 °C, and high-sensitivity Qubit assay (Thermo Fisher) was performed to assess the amount of the amplified cDNA from single cells. We arbitrarily chose three replicates of cDNA amplified on-chip for library construction. For RNA-seq library construction, both single cell and bulk cDNAs were diluted to 250 pg and used to construct indexed libraries using a Nextera XT DNA Sample Preparation kit (Illumina, CA). Libraries were quantified by Bioanalyzer (High Sensitivity DNA analysis kit, Agilent, CA) and Qubit (dsDNA BR Assay kits, Thermo Fisher). The cDNA libraries were sequenced using 101 bases paired-end protocol on Illumina HiSeq 4000 platform. Bioinformatics Postprocessing. Following sequencing, adapter removal was perfomed using atropos v1.1.9.38 Reads were mapped using BWA MEM v0.7.1739 to a reference genome of P. somerae DSM 23386 (a synonym of ATCC BAA1230). This reference (provided in the supplemental files) was reassembled from raw reads obtained from NCBI Sequence Read Archive run accession SRR3947667, using the Unicycler v0.4.640 pipeline with SPAdes assembler v3.11.1,41 and annotated using Prokka v1.1342 and UniFam43 as an additional database. Mapped reads were coordinate-sorted using samtools v1.8.44 Gene expression was then profiled using HTSeq v0.10.0.45 Gene body coverage plots were calculated using Picard Tools v2.18.746 using the CollectRnaSeqMetrics program. To profile potential contaminants, we used the adapter-trimmed reads and called their taxonomy using LMAT v1.2.647 with database kML+H.noprune.4−14.2025, and using its genus-level output. Reads per Kilobase per Million mapped (RPKM) values were verified by using the Rockhopper 8038

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Analytical Chemistry pipeline version 2.0348 to de novo assemble transcripts and calculate their expression values. The RPKM values are comparable in magnitude to the ones in the original data. The table of RPKM expression values and the sequences of the de novo assembled transcripts are provided in the Supporting Information.

according to manufacturer’s instruction to degrade the gDNA released from the P. somerae single cells after the lysis step. In the second test, we proceeded straight onto reverse transcription step after cell lysis without adding gDNA wipeout reagent (Figure 2b). Results did not show significant difference after 2 h of on-chip amplification; however, ∼5× DNA was harvested from the sample without gDNA removal step after 16 h of on-chip amplification. These results verified the efficiency of gDNA wipeout reagent. We analyzed the species origins of the sequencing data to estimate the contamination of the single cell samples. The taxonomy profile was called using Livermore Metagenomic Analysis Toolkit (LMAT), and groups of sequences featuring a hit to the database were sorted into taxonomic hit distributions (Figure 3). The level of contamination was in a range

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RESULTS AND DISCUSSION RNA Amplification in Microfluidic Device. Most library constructions require 25 ng minimum DNA input for subsequent sequencing. To enable standard cDNA library construction for single bacterial cells, we attempted producing sufficient cDNA from a single P. somerae in the microfluidic chip. Studies show that the total RNA per bacterial cell is estimated to be 1.5−200 fg.19,28,49 To investigate the effect of the amount of starting RNA on the amplification yield, we performed a set of experiments to compare the on-chip amplification of 100 pg and 0.1 pg total RNA extracted from a P. somerae population, and single P. somerae cells, with each batch of amplification for 2 h under the same conditions (Figure 2a). The RNA extracted from P. somerae was diluted in nuclease-free water to desired concentrations before introducing it into the microfluidic chip for amplification to ensure that each microchamber contains the aforementioned amount of starting RNA. All ten replicates of extracted RNA and 9 out of 10 single P. somerae replicates were successfully amplified. Although the starting RNA amount differed 1000× in these experiments, no statistical difference was observed for the cDNA produced from each batch of on-chip amplification. The comparable amplification results of 0.1 and 100 pg of the extracted bulk P. somerae RNA and a single P. somerae cell RNA suggest that the cell lysis was efficient to release a starting RNA template within the cell. However, if the microchamber volume was further reduced, a higher amount of starting RNA would likely lead to a larger amount of cDNA after amplification.50−52 On the other hand, the actual starting RNA in a single P. somerae cell could be as low as 1.5 fg, and the quality of the RNA template from a single cell could be lower compared to the bulk RNA, these factors can also lead to the low amount of cDNA after amplification. To increase cDNA amplification yield, we attempted the amplification with extended time (Figure 2b). In addition to performing SC-WTA for 2 h, as recommended, we performed the on-chip amplification for 16 h. Still, this did not increase the average amount of cDNA produced. We conducted another experiment with an initial 2 h amplification of single P. somerae cells, followed by a second round of on-chip amplification by introducing additional polymerase into the microchamber and incubating for 16 h. This led to a 5× increase in the average amount of cDNA produced; however, significant amplification was also detected in the negative controls. To minimize concerns caused by contaminants and duplicated reads introduced by the second round of amplification, cDNA produced from 2 h of on-chip amplification (2−3 ng) was used for low-input library construction and sequencing. The amount of DNA in the negative controls from this experiment was below the detection limit of the high sensitivity Qubit assay (Thermo Fisher) and therefore was not sequenced. To evaluate the possibility of gDNA contamination in the on-chip SC-WTA process, we did additional tests to verify the removal of gDNA before the amplification. In the first test, we used gDNA wipeout reagent provided in the SC-WTA kit

Figure 3. Top contamination sources in amplified transcripts from single cells compared to extracted RNA from bulk cells.

comparable with on-chip MDA-based applications.53 Others have compared amplification biases across various amplification methods and found that MDA-based reaction, specifically Repli-g kit, introduces the lowest amplification bias.21 However, bias inevitably exists because of the capture and nonlinear amplification of minute amount RNA in a single cell.54 In the future, we seek to label target cDNA with unique molecular identifiers to further reduce the intrinsic amplification noise and bias in the library preparation and sequencing,55,56 as well as comparing the results of bacterial and eukaryotic SC-WTA. Figure 4 shows the distribution of normalized sequencing coverage across the transcript for the RNA extracted from the bulk P. somerae and three single cells amplified on-chip and selected for sequencing. The results show that the sequencing coverage between bulk RNA and single cells are comparable, which reconciles with earlier results showing that low-volume MDA reactions tend to reduce amplification bias which effectively improves coverage uniformity.23,57,58 The coverage of assembled transcript of the three single P. somerae cells did not display significant differences (Figure 4b−d), even though the gene expression profile vary largely from cell to cell which will be discussed in details below. Performing the MDA reaction and sequencing a few cells rather than a single cell would likely achieve higher coverage and improve uniformity; however, it would pose challenges of deconvolution during data analysis and, most importantly, it would jeopardize the chance to access transcriptomic data originating from a single bacterial unit of life. Accessing this type of data has the 8039

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Figure 4. Normalized coverage for (a) P. somerae bulk RNA and (b−d) P. somerae single cells.

transcriptome, offering global information as well as cellular heterogeneity. Moreover, the gene expression of the bulk sample is assumed to be the sum of multiple single cell gene expression and could lead to the discovery of some emergent expressions due to cell−cell interaction. We estimated that the sequencing of about 170 single P. somerae cells would lead to the construction of a bulk gene expression profile while still having single cell resolution, based on a combinatoric estimate. Therefore, in this study, three single P. somerae cells are not sufficient to represent the bulk population gene expression due to both small cell number and technical variations. In our follow-up studies, it would be worthwhile to perform a larger scale SC-WTS based on the combinatoric estimate to experimentally reconstruct a bulk-equivalent transcriptome and to examine and minimize the effects of technical variation occurring during amplification and sequencing. The Venn diagram shows the correlation between the number of expressed genes in the three single cells, respectively (Figure 5b). A total of 1133 expressed genes were detected in all three

potential to transform our understanding of how bacteria operate at the single-cell level and how disparate their activities can be, despite the fact that they may live in the same colony and be genomic clones. We compared the ratio of expressed genes detected in the three single P. somerae cells with the bulk RNA (Figure 5a). On average, 1517 expressed genes were detected in single P. somerae cells, which is 74.7% of all the expressed genes detected in the bulk RNA sample. Compared with earlier work by others, the percentage of expressed genes detected in single P. somerae cells was ∼10% higher than the percentage of the expressed genes detected in single eukaryotic cells.31 When grouping single cell results, the total number of expressed genes detected in every two single P. somerae cells reached 1756, which is 86.5% of the total expressed genes detected in the bulk. We found that 1850 expressed genes were detected in three cells, making up of 91.1% of those in the bulk. This trend shows that by sequencing a very small number of single cell libraries, it is possible to reconstruct a bulk equivalent 8040

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Figure 5. (a) Comparison of the total number of genes detected in single P. somerae cells and P. somerae bulk RNA. Detected gene ratio refers to the number of expressed genes detected in single cell versus the number of expressed genes in the bulk RNA. (b) Venn diagram showing total number of genes detected in each of the three single P. somerae and the overlapped detection among the three cells.

Figure 6. (a) Genes sorted based on the descending order of expression level (RPKM) in the bulk sample. Genes expressed in the single cells did not show correlation with those in the bulk. (b) Genes sorted based on the descending order of expression level (RPKM) in single cells and bulk sample, respectively.

medium expression level (100−1000 RPKM), while only 2.5% displayed relatively high expression level (>1000 RPKM) and 4.3% displayed low expression (1 RPKM, and only 0.5−1.2% of the genes had >1000 RPKM expression level, which echoes the finding done by fluorescent in situ hybridization that single bacterial cells display zero

cells, which makes up of 55.8% of the ones in the bulk sample, and 4−5.5% of the expressed genes were detected in only one of the cells. It is likely that these transcripts are low-abundance or expressed in a small fraction of cells. The results display that bacterial single cell RNA-seq could potentially enable the detection of genes that are below the detection limit in the bulk sample or are in low-abundance. Note that there was only 0.3% rRNA detected in the RNA extracted from the bulk P. somerae sample and 0.005% in the single cells. Based on earlier studies, the low amount of rRNA in P. somerae is mostly linked to its slow growth rate.59 To the best of our knowledge, there is no evidence that P. somerae is a species that has high polyploidy, and others have found that polyploidy is often tightly linked with cell size, although not proportionally.60 We sorted the detected genes in the order of the ones with highest to the lowest RPKM in the bulk sample (Figure 6a). In the bulk sample, 99.8% of the genes showed a certain number of transcripts, among which 93.2% of the genes displayed 8041

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Figure 7. Comparison of bulk and single cell gene expression profiles, indicating lack of correlation between the bulk and single cell gene expression level.

Figure 8. Top 20 expressed genes in the bulk RNA and each of the single cells, displaying the heterogeneity of the expression level.

transcripts for many genes and the expression is stochastic.61 Earlier reports show that bacterial gene expression is influenced by a number of cellular parameters and growth conditions, including cell size and growth rate, which further affects other factors such as gene copy numbers and RNA polymerase and ribosomal content.62,63 It is worthwhile to mention that 0.2−0.7% of genes of the single cells had very high expression level (>10000 RPKM). We also report the correlation coefficient values for each of the single cells (Figure 7), the results showed that the bulk sample and single cell gene expression values are not correlated. Also, note that the detectable RPKM level largely relies on the efficiency of RNAseq technology. In this study, 0 RPKM indicates no gene expression was detected rather than the gene had no expression due to the levels of detection of current RNA-seq technology. In addition, technical variabilities have not been

rigorously excluded as a source of RPKM variation in the single cell results, and it is worthwhile to determine the sources of the variations and characterize their impact on the sequencing data in the future. We showed the top 20 expressed genes detected from each of the single cells and the bulk sample in Figure 8. The results show that each cell is transcriptomically distinct and that a bulk analysis assumes a transcriptomic homogeneity would be erroneous. Large scale studies will be able to provide much needed clarity into how much gene expression heterogeneity is present within clonal bacterial populations, between clonal populations, through space, through time, and disturbances inflicted by environmental changes and antibiotic exposures. We anticipate that the thorough understanding of single-cell bacterial gene expression will revolutionize our understanding 8042

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ACKNOWLEDGMENTS

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REFERENCES

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.8b04773. Components and concentrations of reagents (Tables S1−S5) and a description of bioinformatic methods used in this work including reference genome and annotations, RPKM expression values, and sequences of de novo assembled transcripts (PDF)

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This work was supported by the Ivan Bowen Family Foundation and by CTSA Grant Number KL2 TR002379 from the National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. In addition, we thank the Microbiome Program and the Center for Individualized Medicine at the Mayo Clinic for their support, and Dr. Alexander Revzin at the Mayo Clinic for granting us access to his microfabrication facilities.

CONCLUSIONS Single cell whole transcriptome sequencing has found various applications in eukaryotic cells. However, it has rarely been applied to microbial cells and there are major hurdles, including a minute amount of RNA template insufficient for producing the required amount of cDNA, as well as prominent contamination issues. These challenges can be adequately addressed using microfluidic platforms without resorting to additional rounds of amplification as the reaction volume is confined to nanoscales, thus, minimizing contamination during nontargeted amplification. This work demonstrated singleround P. somerae SC-WTA in a microfluidic platform to generate a few nanograms of cDNA for low-input library construction and sequencing. The results revealed the vast differences of gene expression of single cells that cannot be identified otherwise. This proof-of-concept protocol can be adapted to perform SC-WTS on various bacterial species in wide range of human microbiome studies using microfluidic platforms. In the future, we endeavor to perform a larger scale of bacterial SC-WTS to investigate the minimize technical variation during the amplification and sequencing. Ultimately, we envision that it would be possible to perform bacterial SCWTS on various clinical samples, including surgically collected samples or bacterial cells in fixed tissues, to open up this technology to clinical studies.

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The supporting files mentioned in the SI PDF file (ZIP)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Patricio Jeraldo: 0000-0003-0282-4217 Marina Walther-Antonio: 0000-0002-5531-3712 Author Contributions

Y.L. cultured the cells, designed and fabricated the microfluidic devices, performed the SC-WTA experiments, analyzed the data, prepared Figures 1,,, 2,, 5, 6, and 7, and wrote the manuscript. P.J. did bioinformatics work and analyzed the sequencing data, prepared Figures 3, 4, and 7, and wrote the manuscript. J.S.J. prepared the library and wrote the manuscript. B.E. did the sequencing. J.J. supervised the library preparation and sequencing. M.W.A. supervised the project, analyzed the data, and wrote the manuscript. Notes

The authors declare no competing financial interest. 8043

DOI: 10.1021/acs.analchem.8b04773 Anal. Chem. 2019, 91, 8036−8044

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DOI: 10.1021/acs.analchem.8b04773 Anal. Chem. 2019, 91, 8036−8044