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 .
 for most mammalian pathogens , gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene transcripts from infected tissues . 
 pathogen rna constitutes a tiny portion of the total rna isolated from infected tissue samples . both microarray and rnaseq technologies have difficulties in generating reliable reads for weakly expressed pathogen genes . 
 mutant pathogen strains with reduced in vivo proliferation pose an even bigger challenge . here 
 we describe an in vivo gene expression profiling protocol that is very fast , extremely sensitive and highly reproducible . 
 we developed this protocol during our investigation of the fungal pathogen candida albicans in a murine model of hematogenously disseminated candidiasis . using this protocol , 
 we have documented time courses of dynamically regulated c. albicans gene expression during kidney infection , and discovered unexpected features of gene expression responses to antifungal drug treatment in vivo . 
Introduction
Protocol
1. Preparation of Tissues, Reagents and Instruments
2. RNA Extraction from Infected Tissues
3. Gene Expression Profiling Using Digital Bar-coding
Representative Results
Discussion
Disclosures