QC

QC | SORT-seq

Bioanalyzer plots 

During the processing of SORT-seq plates, we check the amount of aRNA and cDNA and the quality of the samples. The plot it generates is a QC metric to study the concentration and size distribution of library fragments and is the first indication of the quality of the sample.  

The QC results depend e.g. on the cell type that was sorted (some cells have more RNA than others), the quality of the sort (e.g. how many wells are filled with a cell) and the quality of the material.  

The QC data unfortunately doesn’t provide information on the number of cells in your plate and the cells’ viability.  This can only be determined by sequencing. 

Workflow 

After your plates arrive at our lab, we perform reverse transcription and second strand synthesis reaction. Subsequently, the material from all wells of one plate is pooled into a single Eppendorf tube to perform In Vitro Transcription (IVT); a linear amplification step that results in amplified RNA (aRNA).  

We fragment this aRNA and then run it on an Agilent bioanalyzer. Unfortunately, we cannot check the quality of the RNA before amplification, as this would result in the loss of unique transcripts.  

After IVT, we perform another reverse transcription reaction as well as a PCR reaction. During the PCR reaction, the material is again amplified and the right adapters for sequencing are added to the sequences. This results in a DNA library (cDNA) that can be used to send for sequencing.  

We run the cleaned cDNA library on an Agilent bioanalyzer. 

aRNA plot

In every aRNA plot, you first see a peak at 25 nucleotides (nt). This is a marker peak. What should follow is a distinct RNA “bump”.

Depending on the cell type, the quality of the cells, the number of cells, and the quality of the reaction, the amount of RNA can vary significantly. A low RNA yield is in some situations very normal (think: low RNA expressing cells, RNA from nuclei, etc.), while in other situations it may indicate a problem with for example the sort or the quality of the cells.

A small “extra bump” may precede the RNA curve. This is a primer-dimer peak and does not interfere with the quality of your sample.

Also important to mention is the RIN number: this number usually says something about the quality of your RNA and is calculated based on the ribosomal peaks of your total RNA. Since we do not measure total RNA and we fragment the aRNA before running it on the bioanalyzer, the RIN number has no meaning anymore and can be neglected.

Based on the aRNA plots we can determine a a low, mid, or high RNA yield. This is as expected, for examplebased on the cell type, or not. In our experience, a mid- or high-range RNA yield usually results in (some) useful data. However, with a high aRNA yield, it is still possible that only a handful of cells have worked, but that each of these cells has a (very) high RNA content.

A very low yield of aRNA (“flatline” and/or RNA amounts <100pg/µl) is very unlikely to result in useful data and might indicate that none to only very few cells in the plate have worked. This can be due to e.g. low quality of the sorted cells or because the cells did not land in the wells correctly. The data may still be useful if the RNA is isolated from nuclei or cells with a very low complexity.

cDNA plot

In every cDNA plot, there are two marker peaks: one at 35bp and one at 10380 bp. There should be a clear and spiky curve between these markers, that goes up at around 200 bp and is back at baseline around 2,000 bp.

The height of the curve depends mostly on the number of PCR cycles but can be limited by a low RNA yield (a very low to absent RNA yield, which will usually result in a low(er) DNA curve even at the maximal cycle number).

What can we conclude based on the cDNA plots?

Option A: the DNA library looks good: there is likely (some) useful data in this library, which can be sent for sequencing. It is good to keep in mind, however, that a good DNA library may still produce poor data, but this is something we can only determine by sequencing. 

Option B: The DNA library doesn’t look goodand we ran the maximum number of PCR cycles: such a library is always accompanied by a “flat” RNA plot, and very likely indicates that there is none to very little data that can be obtained from this library (possible exceptions: nuclei and very low complexity cells).

With poor-looking QC results, it can be useful to sequence one or more of the libraries, to try and find out together with you why the results are suboptimal. Perhaps the sequencing results can help to determine whether the library/libraries indeed provide(s) low-quality data and what can be improved for a potential next sort.

Plate diagnostics

The plate diagnostic plots of your SORT-seq plates are analyzed separately to review the quality of the sequencing data. This tells you how well each cell worked (endogenous reads, UMIs and genes per cell) and how well the technical handling of the plate worked (ERCC spike in reads).

In the top three plots, your 384-plate is visualized, with each circle representing a well of the plate. The wells in red represent high expression of reads and in blue the wells represent a low expression of reads. This way, the endogenous reads and the ERCC spike in reads across the entire plate can be compared. Spike-ins are synthetic control RNAs that are added during sample preparation to detect technical artifacts. The ratio between the endogenous and ERCC spike in reads shows the wells where the reaction worked. Wells with little or no endogenous reads are depicted in red in this plot

The “total unique reads w/o Spike-In reads” graph shows the number of transcripts detected per cell on the X-axis using a log10 scale. The “cumulative dist genes” graph depicts the number of genes detected per cell represented in a cumulative fashion/plot. Next, the “oversequencing_molecules” graph shows the number of molecules that was sequenced more than once, which is  based on the UMIs. A large bar at 0.0 means that these molecules are sequenced once and not oversequenced.

On the bottom left, the graph with the top expressed genes shows which genes have the highest expression detected in the cells across the plate. On the bottom right, the top noisy genes report the genes that vary the most in all the cells across the plate.

QC | 10x Genomics

We will add information as soon as possible. If you have any questions in the meantime, please contact our lab.

QC | Bulk RNA sequencing

Bioanalyzer plots

During the processing of bulk-seq samples, we check the amount of aRNA and cDNA and the quality of the samples. The plot it generates is a QC metric to study the concentration and size distribution of library fragments and is the first indication of the quality of the sample.

The QC results depend e.g. on the cell type, number of cells per sample, and the quality of the extracted material.

QC workflow

After your samples arrive at our lab, we either perform TRIzol extractions and/or RNA concentration normalization of all your samples, depending on the type of samples you send us (cells in TRIzol vs extracted RNA) and whether or not there is enough starting material for concentration normalization. Next, we perform a reverse transcription and second strand synthesis reaction. Subsequently, the material from all samples is pooled into a single Eppendorf tube to perform In Vitro Transcription (IVT); a linear amplification step that results in amplified RNA (aRNA).

We fragment this aRNA and then run it on an Agilent bioanalyzer. If possible, based on the number of cells provided per sample, we also check the quality of the RNA before amplification. However, for some cell types this may not be possible with samples containing fewer than ~10k cells each.

After IVT, we perform another reverse transcription reaction as well as a PCR reaction. During the PCR reaction, the material is again amplified and the right adapters for sequencing are added to the sequences. This results in a DNA library (cDNA) that can be used to send for sequencing.

We run the cleaned cDNA library on an Agilent bioanalyzer.

Total RNA plot

To check the quality of the input material, we run your samples on the bioanalyzer. In every total RNA plot you first see a marker peak at 25 nt. For mammalian samples, there should be two very distinct peaks at 1500-2000nt and 3000-5000nt approximately. These are the ribosomal 18S and 28S peaks. Peaks between the marker peak and the ribosomal peaks indicate degraded material.

The ratio of the two ribosomal peaks to the total area under the curve determines the RIN value. The RIN can have a value from 1 to 10, with RIN=10 being the least degraded RNA and RIN=1 being completely degraded. We recommend a value of ≥6 (for mammalian samples).

aRNA plot

In every aRNA plot, you first see a peak at 25 nucleotides (nt). This is a marker peak. What should follow is a distinct RNA “bump”.

Depending on the cell type, the quality of the cells, the number of cells, and the quality of the reaction, the amount of RNA can vary significantly. A low RNA yield is in some situations very normal (think: low RNA expressing cells, RNA from nuclei, etc.), while in other situations it may indicate a problem with for example the number or the quality of the cells.

A small “extra bump” may precede the RNA curve. This is a primer-dimer peak and does not interfere with the quality of your sample.

Also important to mention is the RIN number: this number usually says something about the quality of your RNA and is calculated based on the ribosomal peaks of your total RNA. Since here we do not measure total RNA and we fragment the aRNA before running it on the bioanalyzer, the RIN number has no meaning anymore and can be neglected.

cDNA plot

In every cDNA plot, there are two marker peaks: one at 35bp and one at 10380 bp. There should be a clear and spiky curve between these markers, that goes up at around 200 bp and is back at baseline around 2,000 bp.

The height of the curve depends mostly on the number of PCR cycles but can be limited by a low RNA yield (a very low to absent RNA yield, which will usually result in a low(er) DNA curve even at the maximal cycle number).

QC | VASA-seq

We will add information as soon as possible. If you have any questions in the meantime, please contact our lab.

Updated on March 31, 2022

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