gene expression dynamics holds vital information about how a cell responds to environmental changes . in the case of an infecting microbe , gene expression data could provide clinically relevant insights on how a pathogen adapts to the infection environment and causes damage to the host . in the past two decades , gene expression studies both in vitro and in vivo have generated large amount of data and laid a foundation for understanding infection biology . however , current profiling technologies including microarray and rnaseq , are not optimal for profiling of pathogen gene expression during infection . host rna constitutes an overwhelming portion ( usually > 99% ) of the total rna isolated from infected tissue samples . the host rna contributes to high background on microarrays , and dominates sequence reads from rnaseq . pathogen cell isolation from infected tissue , in theory , can help to enrich for pathogen rna , but it poses additional problems : it requires large quantities of infected tissue ; the procedure can be tedious ; and most importantly , it is difficult to conserve the native state or integrity of rna during the lengthy process . in order to generate more comprehensive and accurate data on pathogen gene expression during real infections , we set out to develop a protocol that allows reliable and cost - effective quantification of minority rna species in a mixed rna population . nanostring ncounter is a recently developed platform that makes direct multiplexed measurement of gene expression using digital bar - coded probe pairs . this platform has a sensitivity level comparable to that of qrt - pcr but does not require enzymatic amplification . the technology is not genome - wide , it allows detection of up to 800 different genes in each assay . here we use gene expression profiling study of a major human pathogen , candida albicans , during an invasive infection as an example , to demonstrate : 1 ) technical details of the experimental procedures ( protocol ) , 2 ) the sensitivity , reproducibility and dynamic range of this method ( representative results ) , and 3 ) important considerations for designing and performing experiments based on this protocol ( discussion ) . this protocol can be easily adapted for various pathogens and has been successfully applied in a number of infection models . all animal procedures were approved by the institutional animal care and use committee at the los angeles biomedical research institute ( protocol 011000 ) and carried out according to the national institutes of health ( nih ) guidelines for the ethical treatment of animals . the mice were caged in an aaalac - accredited facility located on the campus of harbor - ucla research and education institute . caging and husbandry was provided according to the guidelines in the us public health service publication " guide for the care and use of laboratory animals " . obviously sick , lethargic mice were segregated from the group and euthanized to minimize suffering . the mice were euthanized by pentobarbital overdose ( 210 mg / kg ) , as recommended by the panel on euthanasia of the american veterinary medical association . inoculate three mice per experimental group intravenously with 1 x 10 yeast - phase c. albicans cells . place each kidney into a screw cap tube , snap freeze in liquid nitrogen , and store at -80 c for later rna extraction . note : the animal experiments were carried out as described in detail in xu et al .. prepare the following reagents and instruments before removing kidneys from the -80 c freezer : open a total rna extraction kit . add 2-mercaptoethanol ( 1% v / v ) to buffer rlt and mix well ( prepare 2 ml for each sample).prepare phenol : chloroform : isoamyl alcohol 25:24:1 ( need 600 l for each sample).label m - type homogenization tubes ( m - tube ) and chill on ice.label 2 ml screw cap tubes , and add approximately 300 l zirconia beads to each tube.check instruments are available : tissue dissociator , beadbeater , tabletop centrifuge with adaptors for 50 ml tubes and 96 well plates , photometer . add 2-mercaptoethanol ( 1% v / v ) to buffer rlt and mix well ( prepare 2 ml for each sample ) . prepare phenol : chloroform : isoamyl alcohol 25:24:1 ( need 600 l for each sample ) . label m - type homogenization tubes ( m - tube ) and chill on ice . label 2 ml screw cap tubes , and add approximately 300 l zirconia beads to each tube . check instruments are available : tissue dissociator , beadbeater , tabletop centrifuge with adaptors for 50 ml tubes and 96 well plates , photometer . add 600 l phenol : chloroform : isoamyl alcohol 25:24:1 to the tubes from the previous step . close the lids tightly and vortex on beadbeater for 3 min in a 4 c cold room . centrifuge the tubes at 15,000 x g for 5 min in a 4 c cold room . carefully transfer the aqueous phase to a new 1.5 ml microfuge tube , mix well with equal volume of 70% ethanol , then load onto the spin column ( load twice ) . wash the spin column once with 700 l buffer rw , followed by twice with 500 l buffer rpe . centrifuge one extra minute in a dry collection tube to remove remaining liquid in the spin column . thaw reporter codeset ( green cap tube ) and capture codeset ( gray cap tube ) on ice . add 130 l hybridization buffer to the reporter codeset , invert to mix and spin down . add 10 g of total tissue rna ( in a volume of 5 l ) to each tube . incubate the reactions at 65 c in a thermal cycler with heated lid o / n ( ~18 hr ) . remove one sample cartridge from -20 c and let it warm to rt . remove two reagent plates from 4 c and centrifuge at 670 x g for 2 min in a tabletop centrifuge . set up the prep station following step - by - step instructions on the screen , select the high sensitivity option . remove the reactions from the thermal cycler and immediately load on the prep station . select to run the high sensitivity program ( 3 hr ) . when the prep station program is complete , apply mineral oil to the bottom of the cartridge ( for generation one ncounter only , later generations do not require this step ) , then load the cartridge onto the digital analyzer . set up the digital analyzer following step - by - step instructions on the screen , select the high resolution scan option . select the high resolution ( 600 fields ) option , run the scanning program ( ~4.5 hr ) . when the scanning program is complete , download the results ( or choose to receive the results by email ) , and import raw data into manufacturer 's software . the software will automatically check data quality and raise flags if the quality of the data falls out of the normal range . perform technical adjustment using the built - in function ( optional , follow instructions in the software ) , then export the data as an excel file . normalize the data using one of the following methods : total counts from all genes in the codeset ; one or a few internal control genes ; geometric mean of highly expressed genes ( see discussion ) . calculate the mean expression values for each gene ( if the experiment was done in replicates or triplicates ) , then calculate the ratio of expression levels among different experimental groups . ( optional ) analyze and visualize the profiling results by built - in functions in the nsolver software or a clustering program such as multiexperiment viewer . use male balb / c mice weighting 20 - 22 g for all studies . inoculate three mice per experimental group intravenously with 1 x 10 yeast - phase c. albicans cells . place each kidney into a screw cap tube , snap freeze in liquid nitrogen , and store at -80 c for later rna extraction . note : the animal experiments were carried out as described in detail in xu et al .. prepare the following reagents and instruments before removing kidneys from the -80 c freezer : open a total rna extraction kit . add 2-mercaptoethanol ( 1% v / v ) to buffer rlt and mix well ( prepare 2 ml for each sample).prepare phenol : chloroform : isoamyl alcohol 25:24:1 ( need 600 l for each sample).label m - type homogenization tubes ( m - tube ) and chill on ice.label 2 ml screw cap tubes , and add approximately 300 l zirconia beads to each tube.check instruments are available : tissue dissociator , beadbeater , tabletop centrifuge with adaptors for 50 ml tubes and 96 well plates , photometer . open a total rna extraction kit . add 2-mercaptoethanol ( 1% v / v ) to buffer rlt and mix well ( prepare 2 ml for each sample ) . prepare phenol : chloroform : isoamyl alcohol 25:24:1 ( need 600 l for each sample ) . label m - type homogenization tubes ( m - tube ) and chill on ice . label 2 ml screw cap tubes , and add approximately 300 l zirconia beads to each tube . check instruments are available : tissue dissociator , beadbeater , tabletop centrifuge with adaptors for 50 ml tubes and 96 well plates , photometer . remove tubes containing kidneys from -80 c freezer and put on ice . add 1.2 ml buffer rlt with 2-mercaptoethanol to each kidney . add 600 l phenol : chloroform : isoamyl alcohol 25:24:1 to the tubes from the previous step . close the lids tightly and vortex on beadbeater for 3 min in a 4 c cold room . centrifuge the tubes at 15,000 x g for 5 min in a 4 c cold room . carefully transfer the aqueous phase to a new 1.5 ml microfuge tube , mix well with equal volume of 70% ethanol , then load onto the spin column ( load twice ) . wash the spin column once with 700 l buffer rw , followed by twice with 500 l buffer rpe . centrifuge one extra minute in a dry collection tube to remove remaining liquid in the spin column . thaw reporter codeset ( green cap tube ) and capture codeset ( gray cap tube ) on ice . add 130 l hybridization buffer to the reporter codeset , invert to mix and spin down . add 20 l of the mix to each of the 12 reaction tubes . add 10 g of total tissue rna ( in a volume of 5 l ) to each tube . incubate the reactions at 65 c in a thermal cycler with heated lid o / n ( ~18 hr ) . remove one sample cartridge from -20 c and let it warm to rt . remove two reagent plates from 4 c and centrifuge at 670 x g for 2 min in a tabletop centrifuge . set up the prep station following step - by - step instructions on the screen , select the high sensitivity option . remove the reactions from the thermal cycler and immediately load on the prep station . select to run the high sensitivity program ( 3 hr ) . when the prep station program is complete , remove the cartridge and seal the lanes with a clear tape . apply mineral oil to the bottom of the cartridge ( for generation one ncounter only , later generations do not require this step ) , then load the cartridge onto the digital analyzer . set up the digital analyzer following step - by - step instructions on the screen , select the high resolution scan option . select the high resolution ( 600 fields ) option , run the scanning program ( ~4.5 hr ) . when the scanning program is complete , download the results ( or choose to receive the results by email ) , and import raw data into manufacturer 's software . the software will automatically check data quality and raise flags if the quality of the data falls out of the normal range . perform technical adjustment using the built - in function ( optional , follow instructions in the software ) , then export the data as an excel file . normalize the data using one of the following methods : total counts from all genes in the codeset ; one or a few internal control genes ; geometric mean of highly expressed genes ( see discussion ) . calculate the mean expression values for each gene ( if the experiment was done in replicates or triplicates ) , then calculate the ratio of expression levels among different experimental groups . ( optional ) analyze and visualize the profiling results by built - in functions in the nsolver software or a clustering program such as multiexperiment viewer . one main challenge for pathogen gene expression profiling in vivo is to get enough reads from pathogen rna . given the low percentage of pathogen transcripts in total rna , large amount of total rna ( > 10 g ) has to be used for each reaction . the platform has a unique advantage in this perspective : it uses both a capture probe and a report probe to increase the specificity , so that the overwhelming amount of host rna does not cause a significant level of noise . as shown in figure 1 ( adapted from ref . 6 ) , background raw counts from an uninfected tissue sample ( red dots ) were all below 10 , while raw counts from two infected tissue samples ( blue dots ) were all above 10 . 6 ) , raw counts from two biological replicates ( blue dots , 48 hr post - infection kidney samples ) had very good correlation , with r - square value equals 0.945 . the platform also provides sufficient dynamic range to encompass natural biological expression levels ( figure 1 , raw counts ranged between 10 and 10 ) . using a probe set specifying 248 environmental response genes , we were able to discern two phases of pathogen gene expression ( figure 2 , adapted from ref . 6 ) : an early gene expression response comprises genes with rna levels significantly different between the inoculum and 12 hr post - infection samples ( p<0.05 and > 2-fold change in expression ) , and a late gene expression response comprises genes with rna levels that are significantly different between the 12 hr time point at 48 hr post - infection samples ( p<0.05 and > 2-fold change in expression ) . these results indicate that c. albicans gene expression is dynamically regulated during invasive infection of a mammalian host . probe counts for two infected ( 48 hr post - infection , blue data points ) and one uninfected ( red data points ) kidney samples are presented as a scatter plot . expression of c. albicans environmentally responsive genes during invasive infection ( adapted from ref . 6 ) . changes in expression levels during mouse kidney invasion for 248 c. albicans genes are presented in a heat map format . mean values of biological triplicates are shown for up - regulation ( yellow ) and down - regulation ( blue ) of genes at 12 , 24 , and 48 hr post - infection relative to mean inoculum levels ( 0 hr ) . color saturation represents the extent of the expression change , with full saturation at 10 fold up- or down - regulation . portions of the heat map are expanded to illustrate representative early up - regulated genes ( top ) , late genes ( middle ) , and early down - regulated genes ( bottom ) . in these portions , we define early expression changes as significant differences between the inoculum and 12 hr samples . we define late expression changes as significant differences between the 12 and 48 hr samples . significance refers to changes of > 2 fold and a p - value < 0.05 . the data for each sample were normalized to rna levels from control gene tdh3 before mean values were calculated . our assignment criteria allow some dynamically regulated genes to fall into both the early and late expression classes . this protocol is developed to optimize transcriptional profiling of infecting microbes using a nanostring ncounter platform . the whole procedure , from tissue collection to expression data , requires less than 48 hr . one key variable in this protocol is the amount of buffer rlt added to the tissue at the very first step . too much buffer will dilute the homogenate and lead to lower rna concentration , while too little buffer may lead to formation of viscous gels after the phenol chloroform extraction step and leave no aqueous phase for rna recovery . the empirically determined optimal volumes of buffer rlt for mouse tissues ( assuming typical sizes ) are : kidney ( 1.2 ml ) , tongue ( 1.0 ml ) and lung ( 2.0 ml ) . for microbial pathogens , especially these grown in filamentous form , the bead - beating step helps to break open thick cell wall to fully release cellular contents . in the elution step , in order to increase the final rna concentration , the first round eluate can be added back to the column , and elute a second time . approximately 100 g of total tissue rna can be recovered from one rneasy spin column ( ~2 g/l x 50 l ) . if larger amount of rna is needed , a second prep can be made from the remaining tissue homogenate . do not dispose of the remaining homogenate until rna concentration has been measured , just in case . depending on the infection model , the inoculum size and the pathogen strain , the percentage of pathogen rna in total tissue rna varies from 0%-2% , and typically falls within the 0.05%-0.5% range . for example , 10 g of total tissue rna from c. albicans infected kidney could generate raw counts equal to that of 10 ng of pure c. albicans rna from an in vitro culture ( 10 ng/10 g = 0.1% ) . because the percentage of pathogen rna in total tissue rna varies in a wide range , it is hard to know the quantity of pathogen rna in a given amount of total rna . to avoid wasting codeset ( for 250 genes x 192 reactions , the cost per reaction is around $ 200 ) on samples with pathogen rna below the detection level , an rna quality control step may be performed to determine the pathogen rna level within each sample . this can be done by either q - rtpcr or a small codeset containing a few housekeeping genes . normalization using pathogen genes is a critical step before the expression profiles can be compared among different samples . 3 . use the geometric mean ( n root of the product of n numbers ) of highly expressed genes . for a large codeset ( > 100 genes ) containing randomly selected probes , using total counts for normalization can be a good choice , because the total counts of a large number of unrelated genes may faithfully reflect the amount of rna input . for a small codeset ( < 100 genes ) containing probes focused on a specific process ( such as hyphal growth , given that hyphal growth genes tend to be co - regulated ) , choosing one or a few housekeeping genes as control for normalization is essential . tdh3 , a robustly expressed metabolic gene , has served well as control for many experiments . the third method is a hybrid of method 1 and 2 , with an emphasis for equal contribution from highly expressed genes . though the ncounter platform is not genome - wide , it allows quantification of expression level for up to 800 genes in a single assay . therefore , choosing the most informative genes to analyze is critical for the success of the profiling . information from published expression and functional data is compiled to screen for genes that are of potential interest to the current study , such as the environmental response genes . this approach allows easy comparison of in vivo profiling data to many existing datasets , hence providing context to data interpretation . a category of genes in a microbe genome , such as all genes specifying transcription factors , kinases or cell wall proteins are chosen for probes . this approach allows identification of novel virulence factors and discovery of novel regulatory relationships based on the in vivo profiling data .