NanoString™: User Guide
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1. TABLE 2 continued Troubleshooting guide Issue Diagnosis Possible Cause Possible Solution All NEG counts are high POS counts high NEG counts high Non specific probe aggregation Repeat experiment setting up hybridizations at room temperature POS counts high NEG counts high Capture probe added too soon Repeat experiment making sure Capture Probes are added last and just before placing at 65 C POS counts low NEG counts high Degraded CodeSet Repeat experiment with CodeSet stored at 80 C until just before use One or two NEG counts are high rest are low POS NEG Endogenous counts don t make sense POS counts high NEG counts high POS counts high one or two NEG counts high endogenous counts look normal Counts for all probes do not fit with expected control behavior or expected biology Reduced ligation specificity Potential cross hybridization with another sequence in sample Wrong RLF used Check programs used and calibration of thermocycler Repeat ligation and hybridization on another machine Continue with analysis ignoring the sequences potentially cross hybridizing Re scan cartridge using correct RLF file including version number One or two endogenous probe counts don t make sense for Plex assays check calibration sample normalization count levels for calibration sample Counts for all probes do not fit wi2. Reporter Probe pair 19 USER MANUAL nCounter Expression Data Analysis Guide nanoStrin T E C HN OF LO CT ES NanoString Technologies Inc CONTACT US SALES CONTACTS 530 Fairview Ave N info nanostring com United States us sales nanostring com Suite 2000 Tel 888 358 6266 Europe europe sales nanostring com Seattle Washington 98109 Fax 206 378 6288 Japan japan sales nanostring com www nanostring com Other Regions info nanostring com ri MAR pu Molec 2012 NanoString Technologies Inc All rights reserved NanoString NanoString T A wl PRO ee LI IIOS hat OLIN Dy UISS Hat VOUTIL di nt Tarhnnlinnine Ine MNarnaCterina lY in tha Initand Ctatac ann j iS Technologies Inc NanoString in the United States and n are tne property oT tnelr P vnel ne Manutacture use vNano gt trIing prodauct s May be subject NanoString from Life Technologies Corporation and other third parties owned by NanoString or licensed to FOR RESEARCH USE ONLY Not for use in diagnostic procedures MAN C0011 03
3. eight are designed as negative controls For each positive hybridization control in vitro transcribed RNA targets are pre mixed with the Reporter CodeSet during manufacturing Six different transcripts are provided each at one of the following concentrations in the 35 uL hybridization reaction 110 fM 27 5 fM 6 9 fM 1 7 fM 0 4 fM and 0 1 fM For each ligation control synthetic small RNAs similar to miRNAs are provided for addition to the miRNA sample preparation reaction as described in nCounter miRGE Assay Manual and should be present in the hybridization at 110 fM and 27 fM respectively These small RNA controls must be ligated and hybridized in the same manner as an endogenous miRNA thus they act as controls for the miRNA species in the experiment from sample preparation through hybridization For ERCC negative control probes target transcripts are absent These negative controls can be used to estimate the non specific background in your experiment as described in the section titled Assessing Background USER MANUAL nCounter Expression Data Analysis Guide miRNA Panel Assay Kit Each miRNA Panel Assay Kit contains probes designed against twelve or fourteen ERCC transcript sequences Six of these sequences are utilized as positive ligation controls and six or eight are utilized as negative controls For each positive control a separate ERCC RNA sequence of similar size to a miRNA is supplied Six different small RNA controls are
4. expressed as the coefficient of variation percentage CV of the replicates Determination of CV can be helpful in identifying experimental outliers and for any given reporter probe is expected to decrease as counts increase WORKFLOW EXAMPLE L For each gene calculate the average counts across a set of samples 2 For the same genes calculate the standard deviation across the same set of samples 3 For each gene divide the standard deviation by the average and then multiply by 100 to obtain CV nanostring 14 T E C HN O LO GIES USER MANUAL Troubleshooting Guide This table presents potential solutions to problems that might arise with nCounter data For further questions or explanations please contact your Field Applications Scientist FAS or support nanostring com Please have the log files downloaded and ready to send for any questions about nCounter data or system performance Issues are marked in color to indicate applicable assay in the following manner O Gene Expression Custom CodeSets Plex Gene Expression Assays and Panels miRNA Panels S miRGE CodeSets TABLE 2 Troubleshooting guide Issue Diagnosis Possible Cause Possible Solution No counts for POS NEG and endogenous probes FOVCounted 0 Binding density 0 Inability to focus Clean oil off objective and cartridge Apply fresh oil to each re scan FOVCounted is high binding density is at least 0 02 Part of CodeSet l
5. of the assay Note that because the Binding Density metric includes many system controls that are independent of your sample it is not a good indicator of how well your sample performed and should only be used as an imaging QC 6 NanoString Technologies NanoString Technologies USER MANUAL CodeSet Internal Controls The External RNA Control Consortium ERCC is a group of industry representatives established to develop RNA control transcripts that can be used to assess technical performance in gene expression assays NanoString has adopted sequences developed and tested by the ERCC for positive and negative hybridization controls The ERCC control sequences are not homologous to any known organism are applicable and transferable in all CodeSets and generate consistent results in gene expression analyses Reporter probes designed against ERCC transcript sequences are pre mixed into every CodeSet and are therefore available for use in data analyses O CAUTION If you plan to use additional ERCC transcripts in your experiments please contact NanoString to verify that they are compatible with assay controls before proceeding TABLE 1 Internal assay controls by assay type Assay Type Positive Controls POS Negative Controls NEG Other Controls mRNA Gene Expression and 6 hybridization 8 hybridization Plex Expression Assays miRGE 6 hybridization 8 hybridization 2 ligation hybridization miRNA Panels 6 ligation hybridi
6. provided such that after dilution and addition to the miRNA sample preparation reaction each will be at one of six different concentrations in the 30 uL hybridization 128 fM 32 fM 8 fM 2 fM 0 5 fM and 0 125 fM These controls must be ligated and hybridized in the same manner as an endogenous miRNA thus they act as controls for the entire miRNA analysis process from sample preparation through hybridization The miRNA Panel Assay Kit also contains probes for a set of species specific mRNA housekeeping genes that do not have to be ligated to be counted Additionally the miRNA Panel Assay Kit contains probes designed against non mammalian small RNAs that can be used to measure RNA purification efficiency if so desired For ERCC negative control probes target transcripts are absent These negative controls can be used to estimate the non specific background in your experiment as described in the section titled Assessing Background Normalization The data produced by the nCounter Digital Analyzer are exported as a Reporter Code Count RCC file RCC files are comma separated text csv files that contain the counts for each gene in a sample The data for each sample hybridization are contained in a separate RCC file Prior to comparing data between hybridizations slight differences in hybridization purification binding efficiency and other experimental variables must be normalized To accomplish this NanoString recommends utilizing the inter
7. targets Calibrator Sample Normalization A reference sample that is used in Plex assays to correct for intra CodeSet probe variability This sample is assayed across all Plex sub CodeSets Capture Probe Biotinylated oligonucleotide complementary to the 3 portion of a target sequence Cartridge A micro fluidic device containing 12 lanes that allows distribution and immobilization of nCounter probes on a solid surface for subsequent imaging and counting by the nCounter Digital Analyzer Cartridge Definition File CDF A file used by the Digital Analyzer to associate user input sample information and a RLF with a cartridge CodeSet A collection of Capture Probes and Reporter Probes designed against specific target sequences CodeSet Content Probes within a CodeSet that hybridize to sequences of interest For custom Gene Expression and miRGE CodeSets this refers to the customer defined endogenous and housekeeping genes probes For miRNA panels this refers to all miRNA and mRNA housekeeping probes ERCC positive and negative control probes are not considered part of the CodeSet Content Digital Analyzer The nCounter Digital Analyzer collects data by taking images of the immobilized fluorescent reporters in the sample cartridge with a CCD camera through a microscope objective lens The Digital Analyzer may collect data at one of four resolutions max high medium and low yielding data for hundreds of
8. then be used to adjust the counts for each gene target and negative controls in the associated lane NOTE For miRGE CodeSets NanoString recommends normalizing MRNA probes relative to the six hybridization only controls and the miRNA probes relative to the ligation controls nanostring 8 T E CHNO O G IES NanoString Technologies USER MANUAL WORKFLOW EXAMPLE 1 For each lane sample calculate the sum average or geomean of the positive controls 2 Calculate the average of the sums averages or geomeans across all lanes 3 Divide this average by the sum average or geomean in each lane to calculate a lane specific scaling factor 4 Multiply all the counts in a lane POS NEG and all gene counts by the lane specific scaling factor Following this technical standardization additional normalization may be required prior to analysis The best normalization strategy for your application will ultimately depend on experimental setup Several simple effective normalization procedures are outlined below along with a brief discussion of their merits and limitations Reference Housekeeping Gene Normalization Reference gene normalization may be performed in order to adjust counts of all probes relative to a probe or set of probes that are not expected to vary between samples or replicates Reference gene normalization assumes that some of the targets sequences recognized by the CodeSet are consistent in their expression levels Th
9. 3 SC ge 13 Usina RN A free Inout Ss retener apena 14 Calculating the Percent Coefficient Of Variation 14 CHAPTER 2 Troubleshooting Guide 15 17 CHAPTER 3 NanoString Glossary ooo ccccccccceccccescresecserereserseteeersesesersestrerseverseseversevversesieertevirertevtreesenereesenes 18 19 VI EEA O nau bolts E EE E A 18 nCounter Digital Analyzer Data Output Terme 19 nanostring T E C H N L OGIE S 0 NanoString Technologies USER MANUAL PREFACE Purpose This user manual details specific information pertaining to the data analysis processes for nCounter Gene Expression assays Conventions Used The following conventions are used throughout this manual and are described below for your reference Note Types Special font formatting is used in this manual Such formatting conventions are used in specific instances as described below TIP Information contained in a Tip may offer heloful suggestions alternative procedures methods and or shortcuts NOTE This note type emphasizes general information IMPORTANT 2 This note type presents essential content indicating that the potential exists for assay failure diminished data quality and or a loss of data if the information presented is ignored WARNING This note type indicates that a potential hazard to your personal safety or the potential for equipment A damage exists BOLD When appearing in text or in a procedure the bold text serves to highlight a specific button key str
10. MPLE For each lane calculate the average of the NEG control counts This is the background threshold Mean Plus Standard Deviation of Negative Control Probes The average plus a multiple of the standard deviation of all negative control probe counts in a lane could be defined as background for that lane In this case the false discovery would be expected to be lower to much lower than a simple average depending on the multiple of the standard deviation used For example average plus three standard deviations will be more stringent than average plus two standard deviations reducing the false positive rate but potentially increasing the false negative rate WORKFLOW EXAMPLE L For each lane calculate the average of all the NEG control counts 2 For each lane calculate the standard deviation of all the NEG control counts 3 Add a multiple of the standard deviation to the average For example multiply the standard deviation by two before adding to the average 4 This is the background threshold Highest Negative Control Probe Value Counts for the negative control probe exhibiting the greatest value within a lane is chosen as background This method may significantly reduce the rate of false discovery relative to using the mean WORKFLOW EXAMPLE For each lane determine the maximum of the NEG counts This is the background threshold Using Candidate Targets as Negative Controls If your CodeSet contains probes for targets whic
11. Stage Position Numerical identifier stage position cartridge scanned always a value from 1 to 6 Binding Density Number of fluorescent spots per um of the lane surface Messages Text output field for Digital Analyzer QCs A binding density greater than 2 25 will generate a warning indication Reporter Counts Data fields containing nCounter Reporter Probe information Endogenous 1 4 Reporter probe pair for a target queried within a given sample For Plex assays each sample is queried against unique reporter probe pairs for each target These probe pairs are identified as Endogenous 1 Endogenous 2 Endogenous 3 and Endogenous 4 Code Class Text field defining nCounter Reporter Probe pairs Positive A probe pair that hybridizes to synthetic RNA targets present in the hybridization reaction at known concentrations Negative Reporter probe pair for which no known target should exist within the endogenous sample Housekeeping Reporter probe pair for an endogenous target expected to vary minimally in expression level from sample to sample Endogenous Reporter probe pair for a target queried within a given sample Message Text field detailing important information to the end user Name Text field defining the gene name associated with an nCounter Reporter Probe pair Accession Text field defining the gene accession information associated with an nCounter
12. TM ng T ECHUNO L oO e lS eS nCounter Expression Data Analysis Guide NanoString Technologies Inc 530 Fairview Ave N Suite 2000 Seattle Washington 98109 www nanostring com Tel 206 5 8 6266 888 558 6266 E mail info nanostring com Molecules That Count Translational Research Gene Expression miRNA Expression Copy Number Variation MAN C0011 03 USER MANUAL nCounter Expression Data Analysis Guide FOR RESEARCH USE ONLY Not for use in diagnostic procedures Intellectual Property Rights This nCounter Analysis System manual and its contents are the property of NanoString Technologies Inc NanoString and is intended solely for the use of NanoString customers for the purpose of operating the nCounter Analysis System The nCounter Analysis System including both its software and hardware components and this User Guide and any other documentation provided to you by NanoString in connection therewith are subject to patents copyright trade secret rights and other intellectual property rights owned by or licensed to NanoString No part of the software or hardware may be reproduced transmitted transcribed stored in a retrieval system or translated into other languages without the prior written consent of NanoString Limited License Subject to the terms and conditions of the nCounter Analysis System contained in the product quotation NanoString grants you a limited non exclusive non tra
13. alibration sample and the corrected counts in a sub CodeSet are calculated for each gene The resulting ratios can then be directly compared across the different sub CodeSets WORKFLOW EXAMPLE 1 For a Plex analysis at least one sample is run as a calibration sample and hybridized processed and counted with each sub CodeSet in a single lane 2 Similar to a standard gene expression analysis first normalize all samples within a sub CodeSet to positive controls to identify any potential lane failures Then normalize all samples within a sub CodeSet to your housekeeping genes A Finally calculate the ratio of the counts in the calibration sample to the corrected counts for each gene in each sub CodeSet The resulting ratios can then be compared across sub CodeSets Assessing Background Accurate estimation of probe background is essential for interpreting expression data NanoString includes several probes in each CodeSet for which no target is expected to be present These negative controls can be used to estimate background values in your experiment but may not capture minor probe specific differences in background for every probe in a CodeSet Using the Negative Control Probes Each CodeSet includes several reporters for which no transcript is supplied These probes are designed against ERCC sequences using the same probe design criteria as all other reporters in a CodeSet These negative controls can therefore be used to estimate systema
14. can serve as an approximation of the total nucleic acid expression within a sample With this method a normalization factor is generated using the sum of all counts in a lane relative to the mean sum of all counts in each lane However if a large number of targets vary or if a few vary by orders of magnitude this method may skew the data inappropriately and not produce representative results This method works best to compare similar sample types cells or tissues with similar overall expression profiles WORKFLOW EXAMPLE L For each lane calculate the sum of all the endogenous gene probe counts Do not include the internal positive and negative controls in this calculation 2 Calculate the average of the sums across all lanes 3 Divide this average by the sum in each lane to calculate a lane specific normalization factor 4 Multiply all the probe counts in a lane by the lane specific normalization factor Average Counts Similar to the method described above for total counts the average of all counts in each sample can serve as an approximation of the total nucleic acid expression within a sample With this method a normalization factor may be generated using the mean of all counts in a lane relative to the average mean of all counts in each lane This normalization method has limitations similar to the Total Counts normalization method WORKFLOW EXAMPLE 1 For each lane calculate the sum of all the endogenous gene probe counts Do n
15. d for each of the lanes based on the geometric mean of counts for the reference gene s in each lane relative to the average geometric mean of counts for the reference gene s across all lanes This normalization factor is then used to adjust the counts for each gene target and controls in the associated lane WORKFLOW EXAMPLE 1 For each lane calculate the geometric mean of the reference genes 2 Calculate the average of the geometric means across all lanes 3 Divide this average by the geometric mean in each lane to get a lane specific normalization factor 4 Multiply all the endogenous counts in a lane by that lane s normalization factor Do not multiply the internal assay controls by this factor Global Normalization With CodeSets containing a large number of probes gt 300 it may be assumed that while some targets may increase or decrease in any given sample these are likely to represent a small portion of the total number of targets assayed and that the overall level of expression within a sample will be the same In these cases global normalization methods which utilize large numbers of reporters to generate normalization factors may be employed USER MANUAL nCounter Expression Data Analysis Guide 10 NOTE It is important to note that if a significant fraction of probes exhibit differential expression from sample to sample this method may not be appropriate Total Counts The sum of all counts in each sample
16. e choice of reference genes to include in the CodeSet is therefore a crucial part of experimental design Nanostring recommends at least three reference genes but the more that are included the more accurate the normalization will be For advice in choosing reference genes please contact Nanostring support or e mail support nanostring com CodeSet content normalization corrects for differences in sample input between assays and is typically performed after positive control normalization While reference genes will also be affected by systemic variability a single step normalization using only the reference genes will not distinguish systemic variability from sample variability Method Because reference genes are often expressed at different levels the geometric mean of the reference genes for each lane should be utilized to calculate scaling factors Like a simple average arithmetic mean geometric mean indicates the central tendency of a set of numbers However unlike a simple average the geometric mean is less sensitive to variation in the magnitude of count levels between probes A geometric mean is obtained by multiplying each value in a data set and then taking the nth root where n is the count of numbers in the set of the resulting product n Geometric Mean x1x2 2 x The average of these geometric means across all lanes is used as the reference against which each lane is normalized A normalization factor is then calculate
17. e geometric mean in each lane to calculate a lane specific normalization factor uk WN Multiply all the counts in a lane by the lane specific normalization factor nanostring T E C HN O LO GIES NanoString Technologies USER MANUAL Plex Expression Assay Calibration Sample Normalization The Plex Expression Assay interrogates multiple CodeSets within a single lane of a sample cartridge CodeSets interrogated within a single tube are referred to as sub CodeSets and are numbered 1 and 2 for two samples or 1 4 for four samples per lane Utilizing a calibration sample s across each sub CodeSet in a single lane at least once within a project allows for the correction of systemic differences that may occur between sub CodeSets Calibration sample s a user specified reference sample normalization is typically performed after positive control and content normalization For advice in choosing a calibration sample for your Plex Expression Assay please contact NanoString support or e mail support nanostring com Method To correct for potential intra CodeSet probe variability at least one sample must be chosen as a calibration sample This calibration sample must be run in a separate hybridization with each sub CodeSet and then processed into a single lane of a cartridge at least one time within a project Once counts are normalized within each sub CodeSet to positive controls and to housekeeping genes a ratio between counts in the c
18. eft out Repeat hybridization Low counts for POS NEG and all endogenous probes FOVCount and binding density cannot be obtained FOVCounted is low binding density at least 0 02 Scanner malfunction Inefficient focusing Download log files and contact support nanostring com Clean oil off objective and cartridge Apply fresh oil to each re scan FOVCounted is high binding density is at least 0 02 RNAse contamination Organic solvent contamination Repeat hybridization after cleaning up sample Cracked coverslip Cartridge dropped cartridge subjected to extreme temperature variations Examine bottom of cartridge for defects Download log files of scan and contact support nanostring com CodeSet concentration too low or CodeSet was degraded Spinning CodeSet too hard or vortexing CodeSet when setting up hybridization Do not spin tubes containing CodeSet in anything but picofuges Mix gently by flicking tubes 15 USER MANUAL TABLE 2 continued Troubleshooting guide Issue Diagnosis nCounter Expression Data Analysis Guide Possible Cause Possible Solution Low counts for POS NEG and all endogenous probes Low counts for POS and all endogenous probes miRNA counts low Housekeeping control counts high Scanner malfunction Sample contamination causing ligation problem Download log files and contact support nanost
19. estaved enenectetsadedessitinteesistinenatsetaselpecevlack divtalnsGeatatbinnsteasadtendobt 6 14 Ise ele TEE 6 Assay QC Metrics iii ee 6 Field of View FOV Counted A e on mI 6 Bindina Densi eocen re ao EEE e Ea aare O aaa A AAE a A EAE EOE 6 Bee EE Mmtornal CONTO anciana daa 7 MRNA Gene Expression COGESEULS nica iii 7 MRE CO otto A 7 MENA Panel Code EE 8 Normalization 0 cccccccccccssesescscssesesesssavasessssssssssasesssssssavavassssasasssavesesessesasasasaisasssssavavasesessasesacauesesescscacacateueeeseees 8 POSI CONO INO UTI UO aan 8 Reference Housekeeping Gene NOrmMaliZation occ ececccsececsceeecccsceesecsesseversecteeenevseveteestesnenss 9 Elo HN o duela o A UZ Pr 9 Plex Expression Assay Calibration Sample Normalization oo narrar 11 Assessing Background iii 11 Using the Negative Control PrODOS escindida 11 Using Negative Control Probes in Plex ASSAY c oiocnoniinnninncininconnoninnnconnnrononr nn ron coronar oran 11 Using Candidate Targets as Negative Control 12 Using an RhNA PreeControl ee 12 Molecules That Count Translational Research Gene Expression miRNA Expression e Copy Number Variation 3 USER MANUAL nCounter Expression Data Analysis Guide Background leg re Le E 13 Differences Between MRNA and MIRNA Drobes ccccccccecsecsccsssecsecsesecsecssessessecessessecsssessessesaseaees 13 Determining the Presence or Absence of Target Transcripts 0 00 00 nnee 13 using ine Background TAreSNO lO setae Aaa Aaaa 1
20. f gene counts generated relative to background counts for that lane This method of making presence absence calls may be utilized regardless of the number of replicates per sample but may have limited statistical power depending on the method used to determine background see the section above for different methods of estimating the background threshold When using this method it is important that all comparisons are between normalized candidate target reporter probes and normalized background counts for that lane WORKFLOW EXAMPLE For each lane determine whether the gene counts are greater than the background threshold count value for that lane If they are then expression for that gene is detected in that sample Statistical Tests To determine with statistical significance whether a target transcript is present in a sample technical or biological replicate assays are necessary minimum n 3 After normalizing replicate assays the probability that the target is above background can be found by a variety of means Below is an illustration of how a Welch s t test may be used Molecules That Count Translational Research Gene Expression miRNA Expression e Copy Number Variation 13 USER MANUAL nCounter Expression Data Analysis Guide t Test The Welch s f test can be used to assess the likelihood that a set of gene counts Is significantly different than the set of background counts This type of t test does not make an assump
21. ference between counts given for each code in the presence and absence of RNA can then be tested for statistical significance WORKFLOW EXAMPLE 1 To use this method you will need to perform nCounter analyses in triplicate using your CodeSet but no sample input This will generate a measurement of how each individual code interacts with components of our system during an experiment 2 Normalize the counts obtained in the RNA free assays to the positive spikes as described above in the Positive Control Normalization section A Perform a one tailed heteroscedastic t test comparing the replicate counts obtained in the presence of RNA to the replicate counts obtained in the absence of RNA This will give you a p value for this test 4 For each gene subtract the average count in the RNA free assays from the average count in the samples containing RNA This subtracts out the unique background for each code from the sample measurements using that code 5 As described above if the p value from the f test is less than 0 05 and the background subtracted measurement is greater than zero then the gene of interest is detected in that sample with a 95 confidence level Calculating the Percent Coefficient of Variation In replicate technical or biological measurements minimum n 3 it may be desirable to analyze the variance of measurement in order to assess the overall precision of the counts for any probe This measure of probe precision can be
22. g Density In order to count accurately the Digital Analyzer only counts the codes that are unambiguously distinguishable If codes overlap with each other in an image the Digital Analyzer does not interpolate what those codes are rather it simply does not count them This provides increased confidence that the molecular counts you receive are from truly recognizable codes Under most conditions discarding a few unrecognizable codes does not impact the data However if there are too many overlapping codes in the image significant data loss could occur To determine image saturation the nCounter Digital Analyzer calculates a Binding Density for each lane as it processes the images The Binding Density is a measure of the number of optical features per square micron The Binding Density is useful for determining whether or not data collection has been compromised due to image saturation Typically the range for binding density will be between 0 05 and 2 25 In this range reporter distribution on the slide surface is such that very few if any reporters on the slide surface overlap enabling the Digital Analyzer to accurately tabulate counts for each reporter species A binding density greater than 2 25 is indicative of a large number of overlapping reporters on the slide surface Thus the counts observed in lanes with a Binding Density greater than 2 25 have had significant numbers of codes ignored affecting quantification and potentially linearity
23. g Technologies nCounter assays are designed to provide a single tube sensitive reproducible and highly multiplexed method for detecting nucleic acid targets across all levels of biological expression These assays provide a method for direct detection of targets with molecular barcodes without the use of reverse transcription or amplification nCounter assays are processed on the fully automated Prep Station and data are collected and tabulated by the Digital Analyzer in sets of twelve This manual describes the data output by the nCounter Digital Analyzer and provides guidance for interpreting and troubleshooting your data Assay QC Metrics All nCounter data contain imaging quality control QC metrics that should be assessed prior to analysis and interpretation of data See the Troubleshooting Guide Chapter 2 or contact NanoString Support for help in identifying the cause of deviations in your data Field of View FOV Counted The nCounter Digital Analyzer images each lane in discrete units called fields of view FOV Optical issues such as an inability to focus due to bubbles or insufficient oiling of the cartridge can prevent successful imaging of a FOV The Digital Analyzer reports the number of FOVs successfully imaged as FOV Counted Significant discrepancy between the number of FOV for which imaging was attempted FOV Count and for which imaging was successful FOV Counted may be indicative of an issue with imaging performance Bindin
24. h are known to have no expression such as in a gene knock out study in your samples of interest it is possible to use these probes to determine background in much the same manner as described above for the NanoString supplied negative control probes These candidate target negatives may be used alone or in conjunction with the NanoString supplied negative controls As more negative control probes are used to estimate background confidence that the determined background threshold accurately represents system background increases Using an RNA free Control To use this method you will need to perform nCounter assays without sample input blanks ideally in triplicate so that statistical tests may be applied see the section below for an example of how an RNA free input assay may be used to assess detection This will generate a measurement of how each individual reporter interacts with components of the system during an experiment This is the most robust means of determining background and a means to assess background in a probe specific manner NanoString Technologies USER MANUAL Background Subtraction Once a background threshold has been determined either in a probe specific or more global manner it is possible to subtract out background counts in order to determine true counts In most cases this is unnecessary especially when overall background is low In cases where probe specific background is high background subtraction may impro
25. lyzer that contains sample information probe information and probe counts Reporter Library File RLF A file used by the Digital Analyzer to associate target name and annotation with a unique fluorescent barcode after imaging and counting Reporter Probe Fluorescent barcoded oligonucleotide complementary to the 5 portion of a target sequence Sub CodeSet One of up to 4 CodeSets that are assayed in a single lane of a cartridge when using the Plex assay Each sub CodeSet is referred to by number e g sub CodeSet 1 sub CodeSet 2 sub CodeSet 3 and sub CodeSet 4 if loading four samples in a Plex assay nCounter Digital Analyzer Data Output Terms Term Definition File Attributes Data fields imported from CDF File Name RCC file name Unique for each lane Sample ID Sample identifier specified by the user in the CDF Sample Date Date information specified by the user in the CDF File Version RCC Collector version number GeneRLF RLF used to associate gene names with counts specified by the user in the CDF Comments Text annotation field specified by the user in the CDF Lane Attributes Data fields generated by the Digital Analyzer during a scan Lane ID Numerical identifier of imaged lane always a value from 1 to 12 FOV Count Total number of FOVs imaged per lane Scanner ID Numerical identifier of the Digital Analyzer
26. nal positive controls that are present in each CodeSet Since these targets are independent of the sample normalizing using these controls will help to eliminate variability unrelated to the sample Positive Control Normalization Positive control normalization can be used to normalize all platform associated sources of variation e g automated purification hybridization conditions etc This type of normalization will not account for differences in sample input between technical biological replicates If the calculated positive control scaling factor described below is outside a range of 0 3 3 it may indicate significant under performance of a lane or lanes Care should be taken when interpreting results from such experiments Method The positive spike in RNA hybridization controls for each lane may be used to estimate the overall efficiency of hybridization and recovery for each lane To do so a lane specific value representative of positive control counts should be calculated e g sum of positive control counts geometric mean of control counts see explanation of geometric mean below etc The average of these calculated values across all lanes is used as the reference against which each lane is normalized A scaling factor is then calculated for each of the lanes based on the calculated value for the positive controls in each lane relative to the average of this value for the positive controls across all lanes This normalization factor may
27. nsferable non sublicensable research use only license to use the proprietary nCounter Analysis System only in accordance with the manual and other written instructions provided by NanoString Except as expressly set forth in the terms and conditions no right or license whether express implied or statutory is granted by NanoString under any intellectual property right owned by or licensed to NanoString by virtue of the supply of the proprietary nCounter Analysis System Without limiting the foregoing no right or license whether express implied or statutory is granted by NanoString to use the nCounter Analysis System with any third party product not supplied or licensed to you by NanoString or recommended for use by NanoString in a manual or other written instruction provided by NanoString Trademarks NanoString Technologies NanoString nCounter and Molecules That Count are registered trademarks or trademarks of NanoString Technologies Inc in the United States and or other countries All other trademarks and or service marks not owned by NanoString that appear in this manual are the property of their respective owners Copyright 2008 2012 NanoString Technologies Inc All rights reserved nanostring T E C HN O LO GIES NanoString Technologies USER MANUAL CONTENTS Me e io PA 5 PU dad 5 Conventions UsEd E 5 el E eaa RI A 5 Contact UTOP VAC e 5 CHAPTER 1 Data Analysis sic ccsicecesceesetscscsrsnnstecrccetstnstatteriedpdn
28. oke or menu option available e Bold text may appear elsewhere to highlight important text or terms e Green text is used to help the reader identify active hyperlinks ITALICS Used to emphasize an important word or expression within the text e Formatting of a book title journal or other documentation e Used to indicate the special or unusual meaning of a word or phrase Procedures Numbered procedures appear frequently providing step by step instruction for accomplishing a task Typically a numbered step provides direction for a specific action and may be followed by the expected response Additional information may be presented in the form of a specific note type bullets screen capture or other image important to facilitate clarity and understanding For example In the next screen the active data entry field is indicated by a green box around it Simply move from one field to the next simply press the desired field on the touchscreen with your finger 1 To add an e mail address press ADD gt gt gt The e mail address keyboard screen appears 2 Enter a valid e mail address and press ENTER The e mail address gets saved Contact Information NanoString Technologies Inc 530 Fairview Ave N Suite 2000 Seattle Washington 98109 USA Tel 206 578 6266 888 558 NANO 6266 Fax 206 578 6288 E mail support nanostring com nCounter Expression Data Analysis Guide Data Analysis Introduction NanoStrin
29. ot include the internal positive and negative controls in this calculation 2 Calculate the average of the sums across all lanes 3 Divide this average by the sum in each lane to calculate a lane specific normalization factor 4 Multiply all the probe counts in a lane by the lane specific normalization factor Geometric Mean With this method a normalization factor should be generated using the geometric mean of the approximately 50 100 targets with the highest counts This minimizes the impact that non expressed targets might have on the normalization factor i e the majority of system noise is excluded from contributing to normalization To normalize to the geometric mean probe counts are reordered from most to least abundant and then a normalization factor is calculated using only robustly expressed genes As with normalization to the sum of all counts described above this method works best to compare similar sample types cells or tissues with overall similar miRNA expression profiles WORKFLOW EXAMPLE 1 Reorder the genes from highest to lowest abundance Make sure the order is identical in all samples For this reordering the average expression of each gene could be used as an index of reordering preferred or a single reference lane could be used as the index For each lane calculate the geometric mean of the top 50 100 expressing genes Calculate the average of the geometric means across all lanes Divide this average by th
30. ring com Check purity of starting RNA samples A260 A230 should be greater than 1 8 Purify sample and repeat ligation and hybridization miRNA counts low Housekeeping control counts high Ligase not added Repeat ligation and hybridization Low counts for endogenous probes but POS counts high miRNA counts low Housekeeping control counts high System controls look normal endogenous probe counts near background Thermocycler problem Sample not added Check programs used and calibration of thermocycler Repeat ligation and hybridization on another machine Repeat hybridization Low counts for ligation controls but POS counts high System controls look normal endogenous probe counts highly variable mRNA counts high miRNA counts low RNA in sample extensively degraded Sample contamination causing ligation problem Repeat hybridization using more sample if available Check purity of starting RNA samples A260 A230 should be greater than 1 8 Purify sample and repeat ligation and hybridization mRNA counts high miRNA counts low Ligase not added Repeat ligation and hybridization mRNA counts high miRNA counts low Thermocycler problem Check programs used and calibration of thermocycler Repeat ligation and hybridization on another machine nanostring T E C HN O LO GI E S NanoString Technologies USER MANUAL
31. th expected control behavior or expected biology Counts for a few probes do not fit expectations others do Wrong CodeSet used Adjustment for probe specific or ligation mediated background not performed for Plex counts not normalized properly to calibration sample Repeat hybridization using correct CodeSet Repeat analysis using nSolver miRNA or making sure sample free background counts are subtracted before normalization Plex check calibration sample normalization method Counts for a few probes do not fit expectations others do Probe was designed against incorrect sequence Contact support nanostring com References Tong W et al Evaluation of external RNA controls for the assessment of microarray performance Nature Biotechnology v 24 no 9 1132 1139 2006 Mestdagh P et al A novel and universal method for microRNA RT gPCR data Normalization Genome Biology 10 R64 2009 Vandesompele et a Accurate normalization of real time quantitative RT PCR data by geometric averaging of multiple internal control genes Genome Biol 3 RESEARCHOO34 2002 nCounter Expression Data Analysis Guide NanoString Glossary This chapter contains definitions of terminology associated with the nCounter Analysis System and RCC file data fields General Term Definition Assay Each nCounter assay is capable of simultaneous single tube multiplexed detection of up to 800
32. thousands of target molecules The number of images FOV taken correlates linearly with the number of reporters counted and this in part determines the dynamic range and level of sensitivity in the system Images are processed internally by the Digital Analyzer and the results are exported as a file that can be downloaded via memory stick Field of View FOV Area of the cartridge surface discretely imaged by the Digital Analyzer A single image the Digital Analyzer uses to count molecules Lane Area of the cartridge surface over which nCounter probes are immobilized Each lane is 2600um x 8000um miRNA Tag Reagent A collection of oligonucleotides that facilitate specific ligation of individual miRNAs to a miRNA specific tag in order to generate CodeSet targets Plex Assay nCounter assay which allows for up to 4 samples to be profiled in a single lane Prep Station An automated liquid handling robot that utilizes magnetic bead based purification to remove unbound CodeSet non target cellular transcripts and other cell debris after sample hybridization In addition to hybridization purification of target probe complexes the Prep Station also automates immobilization of the sample onto an imaging surface for subsequent data collection NanoString Technologies NanoString Technologies USER MANUAL Term Definition Reporter Code Counts RCC File A data file csv output by the Digital Ana
33. tic background counts within any single hybridization reaction Background estimation using these probes may be performed in many ways each with an associated affect on stringency and false discovery rate Below is an example of several methods that may be used to estimate background Using Negative Control Probes in Plex Assay Plex CodeSets each contain sub CodeSets either two or four depending on product configuration Each sub CodeSet is designed to behave as an independent CodeSet and thus negative control probes are included at their standard concentration 25pM in all four or two sub CodeSets Due to this design negative controls counts in the Plex assay should be used as a conservative estimate of background as the counts will likely be increased when compared to an endogenous probe hybridized in the absence of RNA Utilizing an RNA free Control as described on page 12 is the most robust methodology for determining background levels Molecules That Count Translational Research Gene Expression miRNA Expression Copy Number Variation 1 USER MANUAL nCounter Expression Data Analysis Guide 12 Mean of Negative Control Probes The average of all the negative controls in a lane could be defined as background for that lane If this value is used false discovery will be expected to be high since by definition half of the known negative probes will exhibit counts above the average background value WORKFLOW EXA
34. tion of equal variance between test populations but does assume that variance within each test population is normally distributed If the p value of a one tailed heteroscedastic normalized replicate gene counts vs all negative control counts is less than 0 05 and the counts for the target are greater than the background threshold then there is at least a 95 probability that the counts for that gene are significantly above the background threshold The stringency of the presence absence test is affected by the choice of p value confidence level and method of determining a background threshold For example a choice of p lt 0 05 and background defined as the average of the negative controls will be less stringent than a choice of p lt 0 01 and background equal to the highest negative control probe counts WORKFLOW EXAMPLE 1 Using a one tailed heteroscedastic t test calculate a p value comparing the replicate gene counts to the background counts 2 Calculate the average of the replicate gene counts 3 If the average replicate gene counts are greater than the background threshold and the p value is less than your acceptable threshold confidence level then the gene is detectable in the samples Using RNA free Input Assays If you have run an RNA free blank assay the presence or absence of transcripts may also be determined by comparing the counts for each gene in the presence of RNA to the counts for that gene in the absence of RNA The dif
35. ve downstream data analysis such as fold change estimation In general background subtraction will amplify estimations of fold change NOTE For probes exhibiting counts near background levels subtraction may result in negative or zero values being obtained If fold change determination for such a target is desired the value for this target should be set equal to the background level in that lane or some other arbitrary value such as 1 Fold change determinations of genes at or near background levels should be considered an estimate Differences Between mRNA and miRNA Probes nCounter miRNA probes rely on end ligation of specific DNA oligonucleotides to each miRNA a process described in the nCounter miRNA Assay Manual and nCounter miRGE Assay Manual Due to this ligation event average background for miRNA probes can differ from MRNA probes Determining the Presence or Absence of Target Transcripts Once an estimation of background has been obtained a presence absence call for any given reporter probe target may be generated As with the determination of background there are many different methods for assessment of target presence absence and the best method is likely to depend on experimental design and nature of the question addressed by a particular experiment Below are some methods of making presence absence calls Using the Background Threshold The simplest method of determining presence absence of a target is to make a comparison o
36. zation 6 or 8 ligation Between 4 5 mRNA hybridization hybridization house keeping genes 2 optional additional non mammalian miRNA spike in controls mRNA Gene Expression CodeSets and Plex Expression Assays Each mRNA Expression CodeSet contains probes designed against fourteen ERCC transcript sequences Six of these sequences are used as positive hybridization controls and eight are designed as negative controls For each positive control in vitro transcribed RNA targets are pre mixed with the Reporter CodeSet during manufacturing For example six different transcripts are provided each at one of the following concentrations in the 30 uL hybridization reaction 128 fM 32 fM 8 fM 2 fM 0 5 fM and 0 125 fM This range of concentrations corresponds to the expression levels of most MRNAs of interest present in 100 ng of total RNA For ERCC negative control probes target transcripts are absent These negative controls can be used to estimate the non specific background in your experiment as described in the section titled Assessing Background For Plex Assays each sub CodeSet contains ERCC negative control probes Specific guidelines for using Plex negative probes are described in the section Assessing Background miRGE CodeSets Each miRGE CodeSet contains probes designed against sixteen ERCC transcript sequences Six of these sequences are used as positive hybridization controls two are used as ligation controls and
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