Counting cytoplasmic incompatibility factor mRNA using digital droplet PCR

Counting cytoplasmic incompatibility factor mRNA using digital droplet PCR | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Counting cytoplasmic incompatibility factor mRNA using digital droplet PCR View ORCID Profile Lore Van Vlaenderen , View ORCID Profile William R. Conner , View ORCID Profile J. Dylan Shropshire doi: https://doi.org/10.1101/2025.07.30.667682 Lore Van Vlaenderen 1 Department of Biological Sciences, Lehigh University , Bethlehem, Pennsylvania, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lore Van Vlaenderen William R. Conner 2 Division of Biological Sciences, University of Montana , Missoula, Montana, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William R. Conner J. Dylan Shropshire 1 Department of Biological Sciences, Lehigh University , Bethlehem, Pennsylvania, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for J. Dylan Shropshire For correspondence: shropshirejd{at}lehigh.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Wolbachia bacteria inhabit over half of all insect species and often spread through host populations via efficient maternal transmission and cytoplasmic incompatibility (CI), killing aposymbiotic embryos when fertilized by symbiotic males. Wolbachia ’s cifB gene triggers CI in males, while cifA , expressed in females, rescues embryos from CI-induced lethality. In some systems, cifA also contributes to CI induction. CI strength—the percentage of embryos that die from CI—is a key determinant of Wolbachia ’s prevalence in host populations, and cifB mRNA levels in testes generally correlate with CI strength. Yet, cifB ’s rarity can hamper precise quantification, necessitating tissue pooling for reverse transcription quantitative PCR (RT-qPCR) to achieve reliable measurements, obscuring variation at the level of individual insect tissues. Here, we present four RT digital droplet PCR (RT-ddPCR) assays to count rare cifA and cifB mRNA from w Mel Wolbachia in Drosophila melanogaster . These assays count cif transcripts alongside a synthetic spike-in RNA or a D. melanogaster housekeeping gene to normalize for technical or biological variation. These assays have a limit of detection of about 1 cifA and 3 cifB copies per reaction. We expect these methods to be useful for mosquito-control programs that use w Mel to block the spread of pathogens from Aedes aegypti to humans. Moreover, the oligos were designed with homology to cifA and cifB sequences from at least 33 Wolbachia strains, suggesting utility beyond w Mel. These methods will allow researchers to measure cif mRNA levels from individual insect tissues, enabling efforts to pair molecular and phenotypic data at unprecedented resolutions. Importance Wolbachia , a maternally transmitted bacterium, is found in over half of all insect species. Its ability to induce cytoplasmic incompatibility (CI), which prevents Wolbachia -free eggs from hatching, significantly contributes to its high prevalence in host populations. Public health experts use CI to spread pathogen-blocking Wolbachia through mosquito populations, thereby controlling pathogen spread. CI is often weak, resulting in few egg deaths and consequently slowing Wolbachia ’s spread. We recently discovered that weak CI often correlates with low CI factor B ( cifB ) mRNA levels. However, our understanding of CI-strength variation remains limited because cifB is transcribed at low levels, making it challenging to measure in individual insects. Here, we report four RT-ddPCR assays to overcome this challenge. These assays offer high sensitivity for rare targets and maintain accuracy and precision across a wide dynamic range. We expect these tools will enhance efforts to understand CI-strength variation in both natural and applied populations. Introduction Many insects host intracellular bacteria that mothers pass to their offspring ( McCutcheon et al., 2019 ; Moran et al., 2008 ). Among these microbes, the Alphaproteobacterium Wolbachia ( Kaur et al., 2021 ) is one of the most common, found in over half of all insect species ( Weinert et al., 2015 ) and often widespread within host populations ( Kriesner et al., 2016 ). Wolbachia ’s high prevalence within host populations largely stems from its ability to cause cytoplasmic incompatibility (CI) ( Hoffmann et al., 1990 ; Shropshire et al., 2020 ). CI occurs when a symbiotic male mates with an aposymbiotic female, killing the resulting embryos ( Yen and Barr, 1973 ). Conversely, embryos from symbiotic females resist CI, giving Wolbachia -bearing offspring a selective advantage. Two Wolbachia genes orchestrate CI: cifB , expressed in testes, induces CI, while cifA , expressed in ovaries, rescues embryos from CI-induced lethality ( Adams et al., 2021 ; Cooper et al., 2017 ; LePage et al., 2017 ; Shropshire et al., 2018 , 2021b ; Shropshire and Bordenstein, 2019 ; Sun et al., 2022 ). In some systems, both cifA and cifB must be co-expressed in males to induce CI (e.g., LePage et al., 2017 ). Beyond its ecological role, public health experts use CI to spread pathogen-blocking Wolbachia strains through Aedes aegypti mosquito populations, thereby protecting humans from diseases like dengue and Zika ( Hoffmann et al., 2024 ; Simmons et al., 2024 ; Utarini et al., 2021 ; Velez et al., 2023 ). Strong CI is characterized by high embryo mortality and is crucial for Wolbachia ’s spread to high frequencies in both natural and applied populations ( Hoffmann et al., 1990 , 2011 ; Walker et al., 2011 ). However, CI strength can vary significantly with host and Wolbachia genetics ( Cooper et al., 2017 ; Hughes and Rasgon, 2014 ; Poinsot et al., 1998 ; Shropshire et al., 2022 ; Walker et al., 2011 ), male age ( Awrahman et al., 2014 ; Reynolds and Hoffmann, 2002 ; Shropshire et al., 2021a ), mating frequency ( Awrahman et al., 2014 ; de Crespigny and Wedell, 2006 ), diet ( Clancy and Hoffmann, 1998 ; Sinkins et al., 1995 ), temperature ( Bordenstein and Bordenstein, 2011 ; Ross et al., 2020 , 2019 ), and other factors (reviewed in Shropshire et al., 2020 ). While a comprehensive understanding of CI-strength variation remains elusive, cifB -mRNA levels show a strong, though imperfect, correlation with CI strength across diverse Wolbachia - Drosophila associations and with male-age-dependent CI in w Ri-bearing D. simulans ( Shropshire et al., 2022 , 2021a ). Reverse transcriptase quantitative PCR (RT-qPCR) is traditionally used to measure cifA and cifB mRNA levels ( Shropshire et al., 2022 , 2021a ). This method requires purifying total RNA, synthesizing complementary DNA (cDNA) through reverse transcription, and then performing PCR with the inclusion of a fluorescent DNA-binding probe or an intercalating dye like SYBR Green to monitor amplicon accumulation. Each PCR cycle doubles the target amplicon abundance, which in turn doubles the fluorescent signal. Researchers infer the initial quantity of target cDNA by identifying the cycle at which its fluorescent signal crosses a predetermined threshold. While cifA mRNA is typically abundant and readily measured by RT-qPCR, cifB transcripts can be rare ( Gutzwiller et al., 2015 ; Lindsey et al., 2018 ). Detecting cifB commonly requires more than 30 amplification cycles to reach the detection threshold, even when cDNA is derived from samples containing tissues pooled from 15 or more individuals ( Shropshire et al., 2022 , 2021a ). These expression dynamics hinder efforts to correlate molecular data with phenotypic outcomes in individual insects and complicate studies in systems where cifB transcripts are especially rare. Compared to RT-qPCR, reverse transcriptase digital droplet PCR (RT-ddPCR) offers enhanced sensitivity, accuracy, and precision ( Vogelstein and Kinzler, 1999 ). Initially, RT-ddPCR assays share steps with RT-qPCR, including RNA extraction and purification, cDNA synthesis, and setting up PCR reactions with a fluorescent DNA-binding probe or an intercalating dye. However, RT-ddPCR differs significantly in several key ways. First, the reaction is partitioned into thousands of nanoliter-sized droplets. Second, PCR is performed on each droplet for the maximum intended number of cycles. Finally, a microfluidic device with a fluorescence detector measures the fluorescence of each droplet, classifying droplets as positive or negative for the target based on high or low fluorescence intensity, respectively. Assuming targets are evenly distributed across droplets, and that some droplets lack target molecules, Poisson statistics can be applied to estimate the number of targets in each positive droplet. Therefore, RT-ddPCR directly counts the number of target molecules, rather than inferring abundance from variation in fluorescence levels across amplification cycles. RT-ddPCR’s benefits stem from its endpoint detection and partitioning capabilities. Unlike RT-qPCR, which assumes 100% reaction efficiency and infers cDNA abundance from exponential doubling, RT-ddPCR simply measures the presence or absence of a target once the reaction is complete. This eliminates the unrealistic assumption of perfect efficiency, making RT-ddPCR more resistant to PCR inhibitors often introduced during RNA purification and cDNA synthesis. Consequently, this enhances the assay’s accuracy and precision. Moreover, RT-ddPCR significantly boosts measurement confidence. While it is common in RT-qPCR to perform two or three technical replicates per sample, each of the thousands of nanoliter-sized droplets in an RT-ddPCR reaction serves as an individual replicate. This vastly increases the number of technical replicates, leading to greater confidence in the results. Since the limit of detection is determined by the upper limit of the 95% confidence interval from negative controls, this improved confidence directly enhances assay sensitivity. For example, assuming no positive droplets in the negative control, the limit of detection for an RT-ddPCR assay is three target copies from 3,000 droplets with 95% confidence ( Hindson et al., 2011 ; Hoshino and Inagaki, 2012 ; Pinheiro et al., 2012 ). The QX200 ddPCR system used in this study reliably generates between 10 and 20 thousand droplets per reaction, further bolstering confidence. Here, we present optimized methods for the extraction, purification, and processing of RNA from individual D. melanogaster testes to count cifA and cifB mRNA of w Mel Wolbachia using two-step RT-ddPCR. While ddPCR has been used to measure Wolbachia abundance in multiple studies ( Fisher et al., 2019 ; Kakumanu et al., 2024 , 2024 ; Kilpatrick et al., 2024 ; Njogu et al., 2025 ; Njogu and Shropshire, 2024 ), to our knowledge, this is the first application of RT-ddPCR to count Wolbachia mRNA. For each cif gene, we developed two duplex RT-ddPCR assays: cif /spike and cif / β-Spec . The cif /spike assays simultaneously measure cif mRNA levels and RNA processing efficiency using a spike-in RNA, enabling users to account for technical variation between samples. These cif /spike assays have a limit of detection of 1 cifA and 3 cifB copies per 20 µL reaction. In contrast, the cif / β-Spec assays normalize cif transcription against that of D. melanogaster ’s housekeeping gene β-Spectrin (FlyBase ID FBgn0250788) to control for biological variation in transcription, with a limit of detection of 1 cifA and 1 cifB copy per reaction. We expect the cif /spike assays to offer broad utility beyond w Mel-bearing D. melanogaster . The w Mel Wolbachia strain is used in Ae. aegypti for mosquito-borne disease control, and we designed the cif RT-ddPCR oligos with homology to 39 cifA (from 32 Wolbachia strains) and 34 cifB variants (from 27 Wolbachia strains). These methods will significantly enhance the ability to measure cif mRNA in individual insects, particularly when cif genes are expressed at low levels. Results Optimizing RNA purification for low-biomass samples To enable sensitive RT-ddPCR assays for rare cifA and cifB mRNA in individual insects, we first developed and validated an RNA extraction protocol optimized for low-biomass samples. Due to its documented high RNA yields, we decided to extract and purify RNA through phase separation ( Tesfamichael et al., 2020 ; Zhao et al., 2023 ). In brief, the protocol (detailed in Van Vlaenderen and Shropshire, 2025) involves bead-mill homogenization, TRIzol:chloroform phase separation, and isopropanol-ethanol RNA precipitation with a glycogen carrier. A final ethanol reprecipitation step significantly improves RNA purity by reducing both protein (average A260/A280 improved from 1.87 to 1.94; Paired T -test P = 0.041; N = 9) and chemical contamination (average A260/A230 improved from 0.93 to 1.45; Paired T -test P = 9.8e-3; N = 9) without significantly impacting RNA yield (Paired T -test P = 0.44; N = 9) ( Data S1 ). We tested this protocol’s accuracy and precision across samples with different biomass by extracting RNA from 1, 4, 10, and 20 pairs of w Mel-bearing D. melanogaster testes. RNA yield per pair of testes is consistent regardless of the number of testes in the sample ( R 2 = 0.069, P = 0.5), averaging 2.1 ng/μL per pair of testes in 25 μL of low EDTA buffer (52.5 ng total; Fig 1A ). Consequently, the total RNA concentration significantly increases with the number of testes (Pearson’s R 2 = 0.86, P = 2.9e-4; Fig 1B ). The observed slope of this relationship is consistent with the average yield per testis pair (One sample T -test P = 0.79), supporting accurate recovery across biomass levels. However, sample-to-sample variation in RNA yield is higher in groups with a higher number of testes per sample (Pearson’s R 2 = 0.99, P = 4.8e-3; Fig 1C ), indicating reduced precision as biomass increases. These findings demonstrate that this RNA extraction and purification protocol yields about 52.5 ng of RNA per pair of testes across a range of tissue abundances, and is especially precise when extracting from low-biomass samples. Download figure Open in new tab Figure 1. Phase-separation RNA extraction consistently yields about 2.1 ng/µL of RNA per pair of testes. (A) RNA concentration per pair of testes in 25 μL of low EDTA buffer remains consistent regardless of the number of testes in the sample. The dashed black line represents the average RNA yield per pair of testes across all extractions, 2.1 ng/µL. (B) Total RNA concentration significantly increases with the number of testes per sample. The dashed black line indicates the expected linear relationship between total RNA yield and the number of testes, calculated using the average RNA yield per pair of testes as the slope. We used a one-sample T -test to compare the observed and expected slopes. (C) Variability in sample-to-sample RNA yield, as measured by the coefficient of variation (CV), is significantly higher in sample groups containing more testes. (A-C) We quantified RNA concentrations using a Qubit 4 Fluorometer with the Qubit RNA High Sensitivity Kit. Each plot displays the Pearson’s correlation coefficient, the corresponding P -value, and the regression line’s slope (m). Raw data for these analyses are available in Data S1 . Developing generalizable cif RT-ddPCR assays To design primers and probes for the cifA and cifB mRNA of the w Mel Wolbachia strain from D. melanogaster , we first gathered a large set of cifA and cifB sequences using BLAST against an in-house Wolbachia genome database. Next, we constructed multiple sequence alignments (MSAs), generated consensus sequences with variable nucleotide masks, and used Primer3Web to design forward/reverse primers and a fluorescein amidite (FAM)-labeled probe. We iteratively refined the MSAs, selecting sequences based on their similarity to w Mel’s cifA and cifB genes until we identified optimal oligos (see Materials & Methods). Finally, we validated the primers against the NCBI nr database using NCBI Primer-BLAST. This confirmed no off-target binding to humans (taxid: 9606), Drosophila (taxid: 7215), and Wolbachia (taxid: 953). We also used Primer-BLAST to identify homology across various Wolbachia genomes. The cifA oligos show homology to 39 sequences ( Data S2 ), and the cifB oligos to 34 sequences ( Data S3 ), spanning 30 Supergroup A and 3 Supergroup B Wolbachia strains. This broad homology includes Wolbachia in 12 Diptera from the family Drosophilidae: D. ananassae ( w Ana), D. biauraria ( w Biau), D. chauvacae ( w Ack), D. incompta ( w Inc), D. innubila ( w Inn), D. melanogaster ( w Mel), D. santomea ( w San), D. simulans ( w Ri, w Ha), D. sturtevanti ( w Stv), D. teissieri ( w Tei), D. yakuba ( w Yak), and Zaprionus tsacasi ( w Zts). Beyond the Drosophilidae, the oligos were homologous to cif genes from Wolbachia in 5 Syrphidae flies ( Brachyopa scutellaris, Cheilosia soror, Epistrophe grossularia, Microdon myrmicae , and Volucella bombylans ), 3 Tachinidae flies ( Lypha dubia, Rhagoletis cingulata ( w Cin), and Sturmia bella ), 2 Muscidae flies ( Haematobia irritans ( w Irr) and Limnophora tigrina ), 5 other Diptera ( Coremacera marginata, Delia radicum, Liromyza huidobrensis (wLtri), Opomyza germinationis , and Protocalliphoroa azurea ), 3 Hymenoptera ( Aporus unicolor, Ectemnius continuus , and Ophion costatus ) and 3 Lepidoptera ( Hofmannophila pseudospretella, Pheosia gnoma , and Rhopobota naevana ). Notably, cifA oligos are not homologous to cifs in Cheilosia soror . Similarly, cifB oligos lack homology to cifs in D. biauraria, Ectemnius continuus, Epistrophe grossularia, Limnophora tigrina, Microdon myrmicae , and Rhopobota naevana . Evaluating RT-ddPCR droplet differentiation We developed duplex RT-ddPCR assays using the newly designed cif oligos and a commercially available RNA spike-in control kit with hexachlorofluorescein (HEX)-labelled probes (TATAA Biocenter, RS25SI). These assays simultaneously count cifA or cifB and a synthetic spike-in RNA molecule. For both the cifA /spike and cifB /spike assays, droplets separate into two clear populations on the FAM channel, indicating the presence or absence of cifA and cifB ( Fig 2A, B ). However, the HEX channel, which measures the spike-in sequence, exhibits three droplet clusters for both assays. In the cifA /spike assay, an intermediate HEX cluster appears close to the positive spike-in cluster ( Fig 2C ). Conversely, in the cifB /spike assay, the intermediate HEX cluster is positioned near the negative spike-in cluster ( Fig 2D ). Simultaneously analyzing FAM and HEX signals offer crucial insights into these intermediate clusters ( Fig 3A ). For the cifA /spike assay, droplets in the upper HEX cluster have a low FAM signal, while droplets in the intermediate cluster have a high FAM signal ( Fig 3A ). This indicates that the intermediate HEX cluster in the cifA /spike assay contains both the spike-in control and cifA . In contrast, for the cifB /spike assay, the intermediate HEX cluster corresponds exclusively to droplets positive for only cifB ( Fig 3B ). Therefore, these intermediate droplets in the cifB /spike assay are negative for the spike-in sequence. These findings demonstrate that we can effectively differentiate droplets containing no template, a single template ( cif or spike-in), or both templates based on their distinct fluorescence profiles. Download figure Open in new tab Figure 2. cif /spike RT-ddPCR droplet discrimination is robust across annealing temperatures. (A) cifA detection on the FAM channel is consistent across a range of annealing temperatures (62.6°C to 55.6°C) in the cifA /spike assay. (B) cifB detection on the FAM channel is consistent across the same annealing temperatures in the cifB /spike assay. Detection for a synthetic spike-in sequence is consistent across annealing temperatures in both the (C) cifA /spike and (D) cifB /spike assays. Vertical dotted lines delineate the results from 20 μL ddPCR reactions, each containing 2 μL of cDNA template derived from RNA purified from 20 pairs of testes. Data points represent individual droplets, with each reaction analyzed containing no fewer than 10,000 droplets. Droplets exhibiting high amplitude (blue on FAM; green on HEX) indicate the presence of the target template, while low amplitude droplets (gray) represent the absence of the template. NC; no-template control. Raw data are available in Data S4 and Data S5 . Download figure Open in new tab Figure 3. The cif /spike RT-ddPCR assays are accurate and precise. (A, B) Droplets from 12 combined (A) cifA /spike and (B) cifB /spike RT-ddPCR reactions reveal that droplets cluster into four distinct groups based on FAM and HEX amplitudes. Droplets are colored to indicate their content: gray (no targets), blue ( cif only), green (spike only), and orange (both targets). We calculated target concentrations from both single- and double-positive droplets. (C, D) A strong linear relationship between the dilution factor and detected abundance of both targets in (C) cifA /spike and (D) cifB /spike RT-ddPCR reactions confirms both assays are accurate across a broad dynamic range. Each 20 µL RT-ddPCR reaction contained 2 µL cDNA and yielded no fewer than 10,000 droplets. Vertical dotted lines indicate the cif limits of detection, based on the upper limit of the 95% confidence interval from the no-template controls. (E, F) Coefficients of variation (CV) do not significantly vary with concentration in the (E) cifA /spike or (F) cifB /spike assays. (C–F) Statistical significance is denoted as: P > 0.05 (ns), P ≤ 0.05 (*), P ≤ 0.01 (**), P ≤ 0.001 (***), P ≤ 0.0001 (****). Statistical tests are Pearson’s product–moment correlations. Raw data are available in Data S6 and Data S7 . Testing RT-ddPCR efficiency across annealing temperatures To determine the optimal annealing temperature for the cif /spike RT-ddPCR assays, we tested RT-ddPCR performance across a temperature gradient ranging from 56.2°C to 62.2°C. The optimal temperature was defined as that which produced the largest amplitude difference between positive and negative droplets. Fluorescence amplitudes of negative droplets are consistent across temperature treatments for cifA (FAM; ; Fig 2A ), cifB (FAM; ; Fig 2B ), and spike (HEX; ; Fig 2C,D ). Conversely, positive droplet signal amplitude depends on temperature; we detect the highest amplitude for cifA at 56.1°C ( ; Fig 2A ), cifB at 56.1°C ( ; Fig 2B ), and spike at 55.6°C ( cifA /spike; Fig 2C ) and 58.4°C ( cifB /spike; ; Fig D ). However, the separation between positive and negative droplet clusters is unambiguous at all tested temperatures. Based on this performance, we selected 60°C as the standard annealing temperature for all subsequent experiments. Validating cif /spike RT-ddPCR assays To assess the accuracy of the cif /spike assays, we tested each assay against a cDNA dilution series. We derived the cDNA from RNA purified from three samples, each containing 20 pairs of Drosophila testes. We used a 1:5 dilution factor for the cifA /spike assays and a 1:2 factor for the cifB /spike assays. We performed RT-ddPCR across these dilutions using 2 µL of cDNA per 20 µL reaction, and calculated target concentrations from both single-positive and double-positive droplets. For these assays to be practical for analyzing single testes pairs, we needed to reliably detect cifA at the 5 -2 dilution (equivalent to 0.8 testes pairs) and cifB at the 2 -5 dilution (equivalent to 0.625 testes pairs). We detect 374.86 cifA copies/reaction at the 5 -2 dilution ( Fig 3C ) and 8.20 cifB copies/reaction at the 2 -5 dilution ( Fig 3D ). These values are above the limit of detection, defined by the upper limit of the 95% confidence interval for the no-template controls (0.54 cifA and 2.59 cifB copies/reaction, respectively). Furthermore, we observe a strong, positive linear relationship between the dilution level and the measured abundance for both the cifA /spike assay ( cifA Pearson’s R 2 = 0.99, P = 6.88e-12; spike Pearson’s R 2 = 1, P = 2.21e-17; Fig 3C ) and the cifB /spike assay ( cifB Pearson’s R 2 = 0.97, P = 5.16e-9; spike Pearson’s R 2 = 0.99, P = 3.23e-11; Fig 3D ). Collectively, these data confirm that both cifA /spike and cifB /spike assays are suitable for counting mRNA from individual testes pairs and accurately measuring the abundance of their respective targets across a broad range of concentrations. We evaluated assay precision by calculating the coefficient of variation between technical replicates ( N = 2) at each dilution factor. As expected (see Discussion), precision declined as target concentrations decreased for cifA and cifB , although not significantly. For instance, the coefficient of variation for cifA in the cifA /spike assay increased from 0.03% at the 5 0 dilution to 7.98% at the 5 -5 dilution (Pearson’s R 2 = 0.338, P = 0.226; Fig 3C ). Similarly, the coefficient of variation for cifB in the cifB /spike assay rose from 12.29% at the 2 0 dilution to 33.14% at the 2 -5 dilution (Pearson’s R 2 = 0.109, P = 0.522; Fig 3D ). We observed similar patterns for the spike-in sequence in both assays ( cifA /spike Pearson’s R 2 = 0.0615, P = 0.635; cifB /spike Pearson’s R 2 = 0.0387, P = 0.709; Fig 3C, D ) . These data demonstrate that the cif /spike assays exhibit good precision, albeit with lower precision as targets are made rarer. Validating cif / β-Spec RT-ddPCR assays In addition to the cif /spike RT-ddPCR assays, we developed duplex RT-ddPCR assays to simultaneously count cifA or cifB and a D. melanogaster housekeeping gene β-Spec . Similar to the cif /spike assays, representative reactions for both the cifA / β-Spec ( Fig 4A ) and cifB / β-Spec ( Fig 4B ) assays demonstrate clear separation of droplet populations, enabling unambiguous quantification of both targets. We assessed the accuracy of the cif / β-Spec assays using the same methodology as the cif /spike assays. In brief, we prepared serial cDNA dilutions (a 1:5 dilution factor for cifA / β-Spec and 1:2 for cifB / β-Spec ) and performed RT-ddPCR using 2 µL of cDNA per 20 µL reaction. For these assays to be practical for counting cif mRNA from individual testes pairs, they needed to reliably detect cifA at the 5 -2 dilution and cifB at the 2 -5 dilution. We detect 356.79 cifA copies/reaction ( Fig 4C ) and 8.13 cifB copies/reaction ( Fig 4D ) at these respective concentrations. These values are above the limits of detection, defined by the upper limit of the 95% confidence interval of the no-template controls (0.59 cifA and 0.87 cifB copies/reaction). Consistent with the cif /spike assay results, dilution factor and the measured abundance for both the cifA / β-Spec assay ( cifA Pearson’s R 2 = 0.985, P = 1.94e-10; β-Spec Pearson’s R 2 = 0.986, P = 1.59e-12; Fig 4C ) and the cifB /spike assay ( cifB Pearson’s R 2 = 0.98, P = 1.52e-11; β-Spec Pearson’s R 2 = 0.998, P = 2.91e-17; Fig 4D ) are strongly correlated. Collectively, these data confirm that, similar to the cif /spike assays, both the cifA / β-Spec and cifB / β-Spec assays are well-suited for detecting mRNA from individual testes pairs and accurately measuring target abundance across a wide dynamic range. Download figure Open in new tab Figure 4. The cif / β-Spec RT-ddPCR assays are accurate and precise. (A, B) Droplets from 14 combined (A) cifA / β-Spec and (B) cifB / β-Spec RT-ddPCR reactions reveal that droplets cluster into four distinct groups based on FAM and HEX amplitudes. Droplets are colored to indicate their content: gray (no targets), blue ( cif only), green ( β-Spec only), and orange (both targets). We calculated target concentrations from both single- and double-positive droplets. (C, D) A strong linear relationship between the dilution factor and detected abundance of both targets in (C) cifA / β-Spec and (D) cifB / β-Spec RT-ddPCR reactions confirms both assays are accurate across a broad dynamic range. Each 20 µL RT-ddPCR reaction contained 2 µL cDNA and yielded no fewer than 10,000 droplets. Vertical dotted lines indicate the cif limits of detection, based on the upper limit of the 95% confidence interval from the no-template controls. (E, F) Coefficients of variation (CV) significantly increase as samples are diluted in the (E) cifA / β-Spec assay but not the (F) cifB/ β-Spec assay. (C–F) Statistical significance is denoted as: P > 0.05 (ns), P ≤ 0.05 (*), P ≤ 0.01 (**), P ≤ 0.001 (***), P ≤ 0.0001 (****). Statistical tests are Pearson’s product–moment correlations. Raw data are available in Data S8 and Data S9 . Next, we assessed the precision of the cif / β-Spec assays by calculating the coefficient of variation between technical replicates ( N = 2) for each dilution. The coefficients of variation for cifA significantly increase from 1.64% at the 5 0 dilution to 89.74% at the 5 -6 dilution (Pearson’s R 2 = 0.771, P = 0.0215; Fig 4E ). Similarly, the coefficients of variation for cifB increase, though not significantly, from 8.01% at the 2 0 dilution to 43.57% at the 2 -5 dilution (Pearson’s R 2 = 0.54, P = 0.0584; β-Spec Pearson’s R 2 = 0.24, P = 0.266; Fig 4F ). For β-Spec , we observed a significant increase in coefficient of variation only in the cifA / β-Spec assay, which encompassed a broader concentration range (Pearson’s R 2 = 0.655, P = 0.0274; Fig 4E ). These data confirm that the cif / β-Spec assays are precise, with a predictable decline in precision when counting low-abundance targets. Evaluating DNase-treatment kits Since we confirmed the cif /spike and cif / β-Spec assays are accurate and precise even for rare transcripts, minimizing genomic DNA (gDNA) carryover from RNA purifications becomes crucial. To establish a suitable methodology, we first extracted and pooled RNA from ten individual males, creating two large-volume samples. We then evenly divided each pooled sample and treated their aliquots with one of five different protocols from two Invitrogen kits: DNA-free and TURBO DNA-free. Both kits offered “routine” protocols, which involved adding 1 µL of DNase to the sample, incubating at 37°C for 30 minutes, and then inactivating the DNase with an included EDTA-containing reagent. The “rigorous” protocols for both kits required adding an additional 1 µL of DNase and repeating the incubation before inactivation. For the DNA-free kit, we also tested a “rigorous x2” protocol, which performed the rigorous treatment twice. After DNase-treatment, we assessed gDNA presence using two approaches. First, we amplified a non-transcribed region of D. melanogaster ’s 28s rDNA using standard PCR and visualized the amplicon via gel electrophoresis. We observe a 28s-associated amplicon when using the DNA-free routine kit; all other protocols yield no detectable DNA ( Fig 5A ). Second, we used the newly designed cifA / β-Spec RT-ddPCR assay to measure the amount of gDNA contamination. In non-DNase-treated RNA samples, we measure 32,141 cifA and 812 β-Spec copies per reaction, indicating high levels of gDNA contamination. All five DNase-treatment protocols significantly reduce the concentration of cifA by at least 95.15% and β-Spec by at least 96.93% ( Fig 5B ). However, gDNA is still detected with the DNA-free routine (1,558 cifA and 25 β-Spec copies per reaction) and rigorous (548 cifA and 6 β-Spec copies per reaction) protocols, suggesting incomplete DNA digestion. In contrast, we detect 0 cifA copies per reaction with the DNA-free rigorous x2 protocol and both TURBO protocols. Only the TURBO rigorous protocol completely removed β-Spec gDNA contamination. Among the three protocols that completely remove cifA gDNA, cifA and β-Spec concentration are similar after reverse transcription ( Fig 5C ). Therefore, we conclude that treating purified RNA from testes extracts with the TURBO rigorous protocol is sufficient to digest all gDNA. Download figure Open in new tab Figure 5. Treatment with TURBO DNase can completely remove contaminating DNA from RNA samples. (A) We could not amplify D. melanogaster 28s rDNA in RNA treated with either the rigorous DNA-free or any TURBO DNase protocols. In contrast, the DNA-free routine protocol showed a visible 28s-associated amplicon. Black vertical lines indicate locations where the gel was cropped for display. (B) All DNase treatment protocols reduced cifA and β-Spec gDNA copy numbers compared to non-DNase-treated controls. We detected no cifA gDNA in the DNA-free rigorous x2, TURBO routine, and TURBO rigorous protocols. Only the TURBO rigorous protocol completely removed β-Spec gDNA. (C) Among the protocols that achieved complete cifA gDNA removal, cifA and β-Spec RNA concentrations after reverse transcription are similar. Raw gel images and data are available in Data S10 and Data S11 . Discussion CI strength directly influences Wolbachia prevalence in natural and applied insect populations ( Hoffmann et al., 1990 , 2011 ), and stronger CI often correlates with higher cifB transcript levels ( Shropshire et al., 2022 , 2021a ). The naturally low abundance of cifB transcripts ( Lindsey et al., 2018 ), however, necessitates the pooling of tissues from 15 or more individuals to achieve reliable quantification through standard RT-qPCR procedures. Here, we introduce four RT-ddPCR assays that overcome this obstacle. Below, we discuss the appropriate application for each assay, strategies for further improving assay sensitivity and precision, and generalizability beyond w Mel in D. melanogaster . To measure cif -gene expression, we developed two distinct assays for each cif gene, cif /spike and cif / β-Spec , which enable different normalization strategies. The cif /spike assays control for technical variability by introducing a synthetic spike-in RNA to each sample prior to RNA extraction. We assume that any loss of this spike-in RNA during downstream processing (e.g., purification, DNase treatment, cDNA synthesis) is proportional to the loss of endogenous cif transcripts. By measuring the recovery of the spike-in RNA via RT-ddPCR, we calculate processing efficiency for each sample. Multiplying raw cif counts against processing efficiency corrects for technical variation introduced during sample handling. In contrast, the cif / β-Spec assay normalizes cif -transcript counts to those of a stably expressed D. melanogaster housekeeping gene, β-Spec ( Hu et al., 2017 ; Shropshire et al., 2021a ). This method controls for biological variation, such as the amount of starting tissue or overall host transcriptional activity. This relative quantification is particularly useful for assessing the density of cif transcripts relative to host-transcript levels. Before using this assay, it is crucial to confirm that β-Spec expression remains stable across all experimental conditions and treatment groups. If β-Spec levels are variable, an alternative housekeeping gene should be selected. We defined the limit of detection for each assay as the upper limit of the 95% confidence interval of the no-template controls (rounded up); this value establishes the threshold below which a signal is indistinguishable from background noise. We calculated limits of detection as 1 cifA copy for both assays, 3 cifB copies for the cifB /spike assay and 1 cifB copy for the cifB / β-Spec assay. Since these values are lower than the transcript quantities measured in dilutions equivalent to individual testes pairs, we conclude these assays are sufficiently sensitive to measure even rare cif transcripts from individual insect tissues. However, the limit of detection can vary between experiments depending on background signal levels, which are influenced by lab cleanliness and handling techniques. Therefore, we strongly recommend including both no-template and gDNA elimination controls in every run. Furthermore, while both assays are accurate and precise, precision is marginally lower when counting rare targets. This result is consistent with our prior measurements of Wolbachia abundance with ddPCR ( Njogu et al., 2025 ), and is an expected consequence of stochastic variation during pipetting with rare targets and low volumes. Although the assays are highly sensitive and precise, their performance can be further improved by increasing the number of molecules analyzed. This can be achieved by eluting the RNA in a smaller volume than the current 25 μL, increasing the current 2 μL of cDNA per reaction to the maximum allowable volume of 7.5 μL for the cif /spike or 8 μL for the cif / β-Spec assays, and increasing the number of droplets analyzed by performing multiple RT-ddPCR reactions for each sample. While the cif / β-Spec RT-ddPCR assays are specific to D. melanogaster , the cif /spike assays do not measure any host transcripts, making them host-independent. Therefore, we expect the cif /spike assays to be applicable to biocontrol programs that deploy w Mel Wolbachia in Ae. aegypti mosquitoes to control viral pathogens such as dengue and Zika (e.g., Utarini et al., 2021 ). Since strong CI facilitates w Mel’s spread, and weak CI can lead to failed public health initiatives (e.g., Ross et al., 2019 ), these assays may enable efforts to monitor cif -transcript levels in field-caught mosquitoes as a proxy for CI-strength variation. Furthermore, we designed the cif oligos with homology to at least 39 cifA variants (from 32 Wolbachia strains) and 34 cifB variants (from 27 Wolbachia strains). This extends application of these assays to an array of Wolbachia -host systems, including agricultural pests like Delia radicum ( Lopez et al., 2018 ; Sontowski et al., 2022 ), Liriomyza huidobrensis ( Hidayanti et al., 2022 ; Ohata et al., 2025 ), and Rhagoletis cingulata ( Schuler et al., 2016 ) that destroy crops such as cabbage, peas, beans, and cherries; the livestock pest Haematobia irritans ( Madhav et al., 2020 ); and Hofmannophila pseudospretella , which damages stored cereals, fabrics, and dried fruit. We recommend performing pilot experiments to confirm the assays’ suitability before applying it to any new system of interest. Materials & Methods Insect lines, care, and maintenance We performed experiments using w Mel-bearing D. melanogaster from the y 1 w * stock (BDSC 1495). We maintained flies under a 12:12 light:dark cycle at 23°C within a Drosophila incubator (Percival DR-36VL) using standard narrow Drosophila vials (Flystuff 32-113RL) containing 7 mL to 10 mL of fly food (Detailed protocol in Wheeler et al., 2024 ). We anesthetized adult flies with CO 2 during experiments. We periodically tested Wolbachia cytotypes by extracting DNA from pools of three randomly sampled flies from each stock using a SquishBuffer method. This was followed by PCR amplification of the Wolbachia surface protein gene and the host 28s rDNA gene and gel electrophoresis (Detailed protocol in Cooper and Shropshire, 2024 ). Sample collection, RNA extraction, and RNA processing We detail the following methods in full on protocols.io (Van Vlaenderen & Shropshire 2025a). To collect samples, we anesthetized non-virgin males with CO 2 , dissected their testes in 1x RNase-free PBS (Fisher Bioreagents, BP3994), and transferred tissue to 2 mL centrifuge tubes (Eppendorf, 05414203) containing 800 µL of chilled TRIzol (Invitrogen, 15596026) and three 2.8 mm ceramic homogenizing beads (VWR, 10158-554). We immediately homogenized samples in a bead-mill homogenizer at 1,500 rpm for 2 minutes (Benchmark Scientific, BeadBlaster 96), centrifuged them to bring the contents to the bottom of the tube, and froze the samples at −80°C until processing. We extracted RNA using TRIzol:Chloroform phase separation. We initially thawed samples, homogenized them again for 2 minutes at 1,500 rpm in a bead mill, and incubated them for 5 minutes at room temperature. We diluted a synthetic spike-in RNA sequence (TATAA Biocenter, RS25SI) 1:64 and added 1 µL to each sample. After adding 160 µL of Chloroform (Thermo Fisher Scientific, 032614.K2), we incubated samples for 5 minutes at room temperature, centrifuged them at 4°C for 15 minutes at 12,000 × g , and collected the upper aqueous phase. We added 3 µL of glycogen (20 µg/µL; Invitrogen, 10814010) and 400 µL of isopropanol (Thermo Fisher Scientific, 327272500) to each sample, incubated them for 10 minutes at room temperature, and centrifuged them at 4°C for 20 minutes at 12,000 × g . We then discarded the supernatant and washed the pellet four times with 500 µL of 75% ethanol (Koptec, V1001), performing intermittent 5-minute centrifugations at 4°C and 7,500 × g . After air-drying the RNA pellet, we dissolved the RNA in 25 µL of low EDTA TE buffer (Quality Biological, 351-324-721). We performed reprecipitation to further clean the sample by adding 62.5 µL of 200 proof ethanol and 2.5 µL of 3 M NaAc (pH 5.2) to each sample, and stored them overnight at −20°C. Following a 30-minute centrifugation at 4°C and 21,000 × g , we washed the pellets three times with ice-cold 75% ethanol, as described above. After air-drying the RNA pellet, we dissolved the RNA in 25 µL of low EDTA TE. We used a NanoDrop 1C Spectrophotometer (Thermo Fisher Scientific, ND-ONE-W) to evaluate the quality of each sample and a Qubit 4 Fluorometer (Thermo Fisher Scientific, Q33238) with RNA High Sensitivity (HS) Assay Kit (Invitrogen, Q32855) to measure RNA quantity. We performed DNase treatment using three protocols from the DNA-free kit (Invitrogen, AM1906) and two protocols from the TURBO DNase kit (Invitrogen, AM1907). We followed the manufacturer’s recommendations for each protocol. For the DNA-free kit, we tried the “routine” and “rigorous” protocols, and we also performed the rigorous protocol twice. For the TURBO DNase kit, we performed both the “routine” and “rigorous” protocols. For both kits, the routine protocol involved adding 1 µL of DNase and 2 µL DNase buffer to 20 µL of RNA, incubating at 37°C for 30 minutes, adding an inactivation reagent, and transferring the supernatant after pelleting the inactivation reagent. The rigorous protocol differed only in that we added another 1 µL of DNase after the initial 30-minute incubation and incubated for an additional 30 minutes. We initially determined if DNA was removed from the reaction by performing PCR for the 28s rDNA of D. melanogaster and gel electrophoresis (Detailed protocol in Cooper and Shropshire, 2024 ). First-strand cDNA synthesis was performed using the SuperScript IV VILO Master Mix (Invitrogen, 11756050). cDNA was either immediately used for RT-ddPCR or stored at −80°C. Oligo design for ddPCR We designed three new RT-ddPCR oligo sets (primers and probes) for this study, targeting cifA, cifB , and β-Spec ( Table 1 ). To design the cif oligo sets, we retrieved relevant sequences from an in-house collection of Wolbachia genomes using BLAST (e-value 1e-10) with w Mel cif sequences as queries. We generated a multiple sequence alignment (MSA) using Muscle5 ( Edgar, 2022 ) and obtained a consensus sequence with variable sites represented as Ns using Geneious Prime v2024.0.5. We used Primer3web v4.1.0 ( Koressaar et al., 2018 ) to design oligos based on the consensus sequence. We iteratively refined the sequence list through multiple rounds of MSA and oligo design until we acquired oligos matching the criteria below. We used Primer-BLAST to determine the number of cif variants and Wolbachia strains that the cif oligos are homologous to in the NCBI nr database. We designed the β-Spec oligo set using Primer3web based solely on the D. melanogaster sequence (GenBank M92288 ). View this table: View inline View popup Download powerpoint Table 1. RT-ddPCR oligos for measuring cifA and cifB mRNA. Primers were between 18 and 22 base pairs (bp) long, and yielded amplicons ranging from 70 to 150 bp. They also had a GC content of 40 to 60%, a GC clamp of 2, a melting temperature (Tm) of 58 to 62°C, a maximum Tm difference of 2°C, and a maximum of 3 consecutive identical nucleotides (poly-X). Probes were 18 to 30 bp in length with a Tm of 65 to 70°C, a GC content of 30 to 80%, and a maximum of 5 consecutive identical nucleotides. We performed Tm calculations using the SantaLucia algorithm ( SantaLucia, 1998 ) with specified ion and dNTP concentrations. To ensure specificity, we analyzed primers against human (taxid: 9606), D. melanogaster (taxid: 7227), and Wolbachia (taxid: 953) genomes using Primer-BLAST. We purchased premixed primer-probe sets from Bio-Rad at a 900 nM:250 nM ratio. We purchased proprietary oligos for the synthetic spike-in RNA from TATAA Biocenter (RS25SI). RT-ddPCR We conducted all RT-ddPCR reactions in 20 µL volumes. We prepared cifA /spike and cifB /spike duplex reactions using 10 µL of ddPCR Supermix for Probes (no dUTP) (Bio-Rad, 1863023), 1 µL of the relevant cif oligo set, 1 µL spike-in RNA primer mixture, 0.5 µL spike-in RNA probe, 5.5 µL of nuclease-free water, and 2 µL of cDNA template. We prepared cifA / β-Spec , and cifB / β-Spec duplex reactions using 10 µL of ddPCR Supermix for Probes (no dUTP) (Bio-Rad, 1863023), 1 µL of each relevant oligo set, 6 µL of nuclease-free water, and 2 µL of cDNA template. After preparation, we sealed PCR plates with an adhesive film (Bio-Rad, MSB1001), vortexed them for 10 seconds to mix (Four E’s Scientific, MI0101002), and centrifuged them for 2 minutes at 2,204 × g (Eppendorf, 5430-R). We then removed the adhesive film and transferred 19.5 µL of each reaction mixture to a droplet generation cartridge (Bio-Rad, 1864007). We added 70 µL of droplet generation oil for probes (Bio-Rad, 1863005) to the adjacent well, placed a gasket (Bio-Rad, 1864007), and positioned the cartridge in a droplet generator (Bio-Rad, QX200) to create droplets. Next, we transferred the 40 µL droplet wells to a 96-well plate, sealed them with a heat-activated adhesive film (Bio-Rad, 1814040 and PX1), and placed them in a thermal cycler (Bio-Rad, C1000 or S1000) for PCR. Reaction conditions varied across experiments, as described in the results section. After PCR, we maintained samples at 12°C until analysis on a droplet reader (Bio-Rad, QX200). We excluded samples that yielded fewer than 10,000 droplets. We present a detailed RT-ddPCR protocol for cif -transcript analysis on protocols.io (Van Vlaenderen & Shropshire 2025). Statistical analysis and figure generation We used QX Manager software (v.2.1.0; Bio-Rad) to calculate RT-ddPCR target concentration and confidence intervals, and to produce RT-ddPCR plots. We performed all other statistical analyses in R (v.4.4.1) using RStudio (v2024.04.2). To create plots, we used the “ggplot2” package (v.3.4.4) in R ( R Core Team, 2024 ; Wickham, 2016 ). Finally, we used Inkscape (v.1.3.2; Inkscape Developers) to modify figure aesthetics. Funding Statement This work was funded by Lehigh University startup funds and a Lehigh University Faculty Research Grant to JDS. Additional support for WRC’s salary was provided by a National Institutes of Health MIRA award (R35GM124701). Contributions (CRediT Classification) Conceptualization : JDS. Data curation: LVV, WRC, JDS. Formal analysis : LVV, WRC, JDS. Funding acquisition : JDS. Investigation : LVV. Methodology : LVV, JDS. Project administration : JDS. Resources : JDS. Supervision : JDS. Visualization : LVV, JDS. Writing – Original draft : LVV, JDS. Writing – Review & editing : LVV, WRC, JDS. Conflict of Interest Statement The authors declare no competing interests. Data availability All data are publicly available in the supplement of this manuscript. Supporting Information Data S1. RNA extraction and purification measurements . Data S2. FASTA file containing cifA sequences homologous to the cifA RT-ddPCR oligos . Data S3. FASTA file containing cifB sequences homologous to the cifB RT-ddPCR oligos . Data S4. cifA /spike RT-ddPCR annealing temperature measurements . Data S5. cifB /spike RT-ddPCR annealing temperature measurements . Data S6. cifA /spike RT-ddPCR dilution series measurements . Data S7. cifB / spike RT-ddPCR dilution series measurements . Data S8. cifA / β-Spec RT-ddPCR dilution series measurements . Data S9. cifB / β-Spec RT-ddPCR dilution series measurements . Data S10. DNase treatment raw gel images . Data S11. DNase treatment RT-ddPCR results . Acknowledgments We gratefully acknowledge Helene Hartman, Lara Parada-Tixe, and Abby Klebe for their support in the laboratory. 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