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101results：
Q What is the purpose of each DNA clean-up step using beads in the Stereo-seq Transcriptomics Set protocol? What do the beads remove or harvest?
A

Purification Steps

Purposes
0.8X beads (after cDNA release from the tissue)
The high ratio of beads allows binding of cDNA molecules as many as possible, while leaving impurities from the tissue samplein the supernatant.
0.6X beads (after cDNA amplification by PCR)
The beads bind cDNA molecules. Primers and other small DNA fragments will remain in the supernatant and get discarded.
0.55X beads and 0.15 μL beads (after cDNA fragmentation & amplification)
Double selection which removes both larger and smaller fragmented DNA and harvests the intermediate fragments.

0.55xbeads



Q Can the primary antibody staining solution be incubated with the chip at 4℃ for overnight instead of at room temperature for 45 min?
A
We suggest following the protocol with all antibody incubation steps. Since the antibody needs to be incubated with the Stereo-seq Chip T, please avoid leaving the chip with the antibody at 4℃ overnight.
Q Have other secondary antibodies been tested besides Alexa Fluro Plus 2nd antibodies?
A
We have only tested Alexa Fluro Plus 2nd antibodies.
Q Are there any recommended blocking solutions for non-human species?
A
We recommend to use serum and FCR blocking reagent for preparing the blocking solution. For non-human/mouse species, we recommend to substitute FcR blocking reagent with nuclease-free water.


Q Is 3 antibodies with DAPI the maximum co-staining targets we have tested in R&D?
A
Yes, that's the maximum co-staining targets we have tested in R&D.


Q Is Stereo-seq mIF workflow applicable on any chip sizes other than 1cm x 1cm?
A
Currently not, but we are in the process of testing Stereo-seq mIF workflow on other chip sizes besides 1cm x 1cm.


Q As SAW generates multiple GEF files, what information do they store respectively? Do they support visualization?
A
File Name
Common Applications
Visualizations Supported
Description
SN.raw.gef
Transcription
False
Original transcription matrix of whole chip area, containing only bin1 geneExp group.
SN.tissue.gef
Transcription
False
Transcription matrix of tissue-covered region, containing only bin1 geneExp group.
SN.gef
Transcription
True
Visual transcription matrix of whole chip area, containing multiple bin geneExp and wholeExp groups.
SN.cellbin.gef
Transcription, ssDNA/DAPI
True
Cell-gene expression matrix of tissue-covered region.
SN.adjusted.cellbin.gef
Transcription, ssDNA/DAPI
True

Cell-gene expression matrix of tissue-covered region using adjusted cell shapes.

SN.<protein_IF>.gef
Transcription, mIF
True

Original transcription matrix of labeled area, containing only bin1 geneExp group.

SN.<protein_IF>.cellbin.gef
Transcription, mIF
True
Cell-gene expression matrix that is extracted by cellmask of IF image gray scale threshold filtering. [Recommended to name like this, it is not generated by default but needed to switch to cellCut after tissueCut.]
SN.protein.raw.gefProteinFalseVisual matrix of labeled area, only containing bin1 geneExp.
SN.protein.tissue.gefProteinTrueProtein matrix of the tissue cut area, containing multiple bin geneExp and wholeExp groups.
SN.protein.gefProteinTrueProtein visualization matrix of the complete chip area, including multi-bin geneExp and wholeExp.
SN.protein.cellbin.gefProteinTrueCell-protein expression matrix of tissue coverage area


Q How to interpret GEF-format files?
A

Option 1: use C++ compiled geftools:

  • https://github.com/STOmics/geftools

Option 2: use Python package - gefpy (e.g. 0.6.1):

  • https://pypi.org/project/gefpy/

  • https://gefpy.readthedocs.io/en/latest/index.html

  • pip install gefpy==0.6.1

Option 3: with installed SAW sif (e.g. v5.1.3):

  • https://hub.docker.com/repository/docker/stomics/saw

  • singularity exec SAW_v5.1.3.sif cellCut

  • Please use Singularity version 3.8 or later

Bash
export HDF5_USE_FILE_LOCKING=FALSE
## gef2gem using geftools
geftools view -i <SN>.gef -o <SN>.gem -s <SN>
# -i input square bin GEF, e.g.SN.raw.gef or SN.gef
# -o output GEM
# -s SN
## gef2gem using gefpy
python
>>> from gefpy.bgef_reader_cy import BgefR
>>> bgef=BgefR(filepath='<SN>.gef',bin_size=200,n_thread=4)
>>> bgef.to_gem('<SN>.bin200.gem')
## gef2gem using SAW sif
## export SINGULARITY_BIND="/path/to/input/dir,/path/to/output/dir"
singularity exec SAW_v5.1.3.sif cellCut view -i <SN>.gef -o <SN>.gem -s <SN>

## cgef2cgem
geftools view -i <SN>.cellbin.gef -o <SN>.cellbin.gem -d <SN>.raw.gef -s <SN>
# -i input cellbin GEF
# -o output cellbin GEM
# -d input square bin GEF, e.g. SN.raw.gef or SN.gef
# -s SN

## gem2gef
geftools bgef -i <SN>.gem -o <SN>.gef -b 1,20,50 -O Transcriptomics
# -i input square bin GEM
# -o output square bin GEF
# -b bin sizes seqarate by comma, default: 1,10,20,50,100,200,500
# -O omics name


Q What are the purposes of the three Saturation curves?
A

The first one can indicate whether the sequencing is saturated. If the fitted curve reaches or approximates a plateau, this means the sample is about to saturate. Depending on the goal of each individual project, you may need additional sequencing runs. For example, a project designed to recover very lowly expressed transcripts or involves precious samples may desire a higher sequencing saturation. A recommended saturation of 80% is an empirical threshold, it is not a rigid value.

The second and third figures are plotted with statistics computed at bin levels, and their stationary stages are lagging behind Figure 1. The first plot serves as the main indicator for the potential benefit of additional sequencing.


Q What is the difference between the two SAW registration modules, register and rapidRegister?
A

SAW register pipeline includes a cell segmentation procedure, whereas rapidRegister does not.


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