It is recommended to follow the protocol for antibody incubation. Since the antibody needs to be incubated on Stereo-seq Chip T, please avoid leaving the chip with the antibody at 4℃ overnight.

Only Alexa Fluro Plus 2^nd antibodies have been tested.

Currently not. R&D is in the process of testing Stereo-seq mIF workflow on other chip sizes besides 1cm x 1cm.

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

• In an HTML report, saturation analysis is used to evaluate the overall quality of sequencing data. To improve computational efficiency, small random samples are taken from successfully annotated reads at the resolution of the specified bin size (e.g. bin 20 for SAW 8.1). Thus, running the same data multiple times may yield slightly different results, but the overall shape of the curves remains consistent.

• Figure 1: This curve shows the gradual increase of the median number of genes per bin x as the random sampling quantity increases.

• Figure 2: This curve shows the gradual increase of the number of unique reads per bin x as the random sampling quantity increases.

• Figure 3: This curve represents the statistical analysis of Unique Reads in the sampled data. Saturation level is calculated as 1-(Unique Reads)/(Total Annotated Reads). As the sampling size increases, a gradually flattening curve indicates that the data is approaching saturation. Whether additional sequencing is necessary depends on the overall project design and sample characteristics. For instance, precious samples may benefit from further sequencing.

• Note: The x-axis is the same for all three graphs, while the y-axis represents median genes per bin x, unique reads per bin x,  saturation level, respectively. (x=20 for SAW v8.1)

These two modules only exist for SAW < v8.0. SAW register pipeline includes a cell segmentation procedure, whereas rapidRegister does not.

Step 1: Check if the "Valid CID Reads" ratio in the HTML report is lower than 30%. If so, please check whether the FASTQ corresponds with chip SN.

Step 2: Check data under "Clean Reads":

• Reference genome does not meet the format requirement: if the ratio of multi-mapped reads is high, and the uniquely mapped reads ratio is extremely low, please run SAW checkGTF for the GFF/GTF file to verify the file format is valid for running pipelines.

• Contamination: please perform troubleshooting on the wet lab workflow.