Xenium Prime 100 Gene Add-on Panel

Xenium Human 5K Pan Tissue & Pathways Panel

We identified potential risks in your panel design, including

3 critical alerts

8 moderate alerts

5 informational notices

To ensure optimal assay performance, we advise you review the Panel Design Details section below and accept all recommendations.

Custom Genes Summary

Review your custom genes with any important panel design notes or warnings. Genes with warnings are at the top of the list. The "Probe sets" and "Included in Optimized Panel" columns include our optimization recommendations. For more information about these warnings, see the charts in the Panel Design Details section below.

Gene NameEnsembl IDNotes and WarningsProbe setsIncluded in Optimized Panel
AC007906.2ENSG00000277639
-No
IGHG1ENSG00000211896
-No
IGKCENSG00000211592
-No
Gene NameEnsembl IDNotes and WarningsProbe setsIncluded in Optimized Panel
NEAT1ENSG00000245532
4Yes
ACTBENSG00000075624
5Yes
B2MENSG00000166710
5Yes
EEF1A1ENSG00000156508
5Yes
GAPDHENSG00000111640
5Yes
IGHG2ENSG00000211893
1Yes
JCHAINENSG00000132465
1
Yes
LYPD2ENSG00000197353
1Yes
PCP2ENSG00000174788-2Yes
SEPTIN4ENSG00000108387-2Yes
TRBC1ENSG00000211751-2Yes
AVPI1ENSG00000119986-3Yes
BGNENSG00000182492
3
Yes
C11orf96ENSG00000187479-3Yes
CCDC71LENSG00000253276-3Yes
ERVMER34-1ENSG00000226887
3
Yes
GALNT4ENSG00000257594
3
Yes
GPC3ENSG00000147257
3
Yes
INSENSG00000254647-3Yes
NUS1ENSG00000153989-3Yes
PLPP3ENSG00000162407
3
Yes
THSD4ENSG00000187720-3Yes
TPSG1ENSG00000116176-3Yes
ABCA8ENSG00000141338-4Yes
ADAMTS14ENSG00000138316-4Yes
AGR2ENSG00000106541-4Yes
AIF1ENSG00000204472
4
Yes
AMOTL2ENSG00000114019
4
Yes
BPIFB1ENSG00000125999-4Yes
C1QCENSG00000159189-4Yes
C20orf85ENSG00000124237-4Yes
C2orf88ENSG00000187699-4Yes
C7ENSG00000112936-4Yes
CCL2ENSG00000108691-4Yes
CEACAM5ENSG00000105388-4Yes
CENPLENSG00000120334-4Yes
CYP4B1ENSG00000142973-4Yes
DSC3ENSG00000134762
4
Yes
ECM1ENSG00000143369-4Yes
FAM183AENSG00000186973-4Yes
FAM216BENSG00000179813-4Yes
KRT78ENSG00000170423
4
Yes
LUMENSG00000139329-4Yes
LY6G6CENSG00000204421-4Yes
NAMPTENSG00000105835-4Yes
NIBAN3ENSG00000167483-4Yes
PAMR1ENSG00000149090-4Yes
PLXNB1ENSG00000164050
4
Yes
RAB11FIP1ENSG00000156675
4
Yes
SHDENSG00000105251-4Yes
TBC1D10CENSG00000175463-4Yes
TMEM101ENSG00000091947-4Yes
TMEM255BENSG00000184497
4
Yes
TNS4ENSG00000131746-4Yes
ABCA10ENSG00000154263-5Yes
ANKRD33BENSG00000164236-5Yes
AQP1ENSG00000240583-5Yes
BIRC3ENSG00000023445-5Yes
CCL5ENSG00000271503-5Yes
CD3DENSG00000167286-5Yes
CD52ENSG00000169442-5Yes
CDHR4ENSG00000187492-5Yes
CFAP100ENSG00000163885-5Yes
CNN1ENSG00000130176-5Yes
CTSWENSG00000172543-5Yes
DCNENSG00000011465-5Yes
ERICH3ENSG00000178965-5Yes
F3ENSG00000117525-5Yes
FLT1ENSG00000102755-5Yes
GREB1ENSG00000196208-5Yes
HEYLENSG00000163909-5Yes
IL7RENSG00000168685-5Yes
ITGA7ENSG00000135424-5Yes
KIAA1324ENSG00000116299-5Yes
KIF18BENSG00000186185-5Yes
LCN2ENSG00000148346-5Yes
LCNL1ENSG00000214402-5Yes
LDLRAD1ENSG00000203985-5Yes
MNDAENSG00000163563-5Yes
MPP1ENSG00000130830-5Yes
MS4A6AENSG00000110077-5Yes
MYH11ENSG00000133392-5Yes
MYL9ENSG00000101335-5Yes
NCCRP1ENSG00000188505-5Yes
P2RX5ENSG00000083454-5Yes
PTPRBENSG00000127329-5Yes
RGL3ENSG00000205517-5Yes
SLCO2A1ENSG00000174640-5Yes
SPRR3ENSG00000163209-5Yes
TIMP3ENSG00000100234-5Yes
TRACENSG00000277734-5Yes
TRIM29ENSG00000137699-5Yes
WNT9AENSG00000143816-5Yes

Your Current Panel shows the state of your current panel design based on the gene list and reference data you provided.

Optimized Panel shows the improved panel design if you accepted all the recommendations under the optimized panel column.

How do I interpret this?

This plot shows the predicted per-cell type TP10k utilization (a combination of all the genes' expression profiles for each cell type) based on the reference dataset you provided.

To ensure optimal results, check the utilization values for each cell type to be below approximately 1600 TP10k. High utilization for a cell type can lead to optical crowding which results in reduced detection sensitivity (the fraction of transcripts detected per cell) and limits accurate quantification of lowly expressed genes in affected cells.

Utilization is high in 1 cell type and may lead to optical crowding

IgG plasma cell exceeds the recommended limit of 1600 TP10k. Optical crowding in this cell type may result in somewhat degraded assay performance.

Note

To avoid optical crowding in cell types with high utilization, we recommend excluding or reducing probe set coverage for extremely highly expressed genes.

Please see Panel Utilization for Highly Expressed Genes for our recommendations on what you can do with specific genes.

How do I interpret this?

This plot shows the predicted per-gene TP10k utilization alongside the corresponding cell types associated with each gene. You can change how many genes are shown by using the slider, up to the top 25 most highly expressed genes per cell type.

To ensure optimal results, check that the utilization values for each gene to be below approximately 120 TP10k. High utilization for a gene can lead to optical crowding which results in reduced detection sensitivity and limits accurate quantification of lowly expressed genes in affected cells.

We also recommend that you do not include genes which are moderately expressed in a large number of cell types, as doing so can limit the optical budget available in those cell types without providing much information.

5 genes are broadly and highly expressed and may lead to reduced assay performance

ACTB, B2M, EEF1A1, GAPDH and NEAT1 are moderately expressed in many cell types and may limit your ability to design a performant panel.

2 genes should be removed due to high expression

We suggest changes to IGHG1 and IGKC in order to avoid optical crowding issues.

If you choose to include genes that are this highly expressed, there is a high risk of decreased sensitivity in the affected cells. See an example here.

Probe sets of 2 genes should be reduced due to high expression

We suggest changes to IGHG2 (3) and JCHAIN (2) in order to avoid optical crowding issues.

Alternatively, you can replace the affected gene(s) with lower-expressed genes by modifying your gene list. If you choose to include highly expressed genes, there is a potential risk of decreased sensitivity and a limited ability to accurately quantify lowly expressed genes in the affected cells.

Note

We will leave ACTB, B2M, EEF1A1, GAPDH and NEAT1 on your panel, which are broadly/highly expressed (unless other recommendations specifically suggest removing them for optimization). Depending on your research question, this may or may not be problematic. To maximize your panel's utility, we recommend you choose one of the following options, if possible:

  • Reduce the number of probe sets to prevent them from contributing to optical crowding across many cell types
  • Remove these genes and replace them with more cell type specific genes

Learn more in our Xenium Add-on Panel Design Technical Note.

How do I interpret this?

This plot shows the number of probe sets for each gene on your panel. The effectiveness of gene detection and subsequent sensitivity is correlated with the number of probe sets used.

To ensure your genes have robust detection, make sure they have at least three probe sets.

For details on how probe sets are assigned to genes, please view our support site. In some instances, the number of probe sets will be less than the default of 5 because we either could not design that many probe sets for a gene, or because one or more of the probe sets were removed as they were predicted to interact with other probes on your panel. We show this, and any recommended changes to the requested number of probe sets, using specific shapes in the following plot. You may click on the legend entries to filter what is shown in the following plot.

Recommendation: These are genes with probe sets which we recommend adjusting in order to achieve an optimal optical budget in your final panel. This will generally reduce the sensitivity of the individual gene, but ensure other co-expressed genes retain high sensitivity by limiting optical crowding.

At limit: These are genes which are using all available probe sets designed by 10x Genomics.

3 genes may have reduced sensitivity

IGHG1 (1), IGHG2 (1) and LYPD2 (1) have a small number of probe sets on your panel. Sensitivity may be lower than expected for these genes.

Note

We cannot more than the recommended minimum of 3 probe sets for IGHG2 (1), JCHAIN (1) and LYPD2 (1) (for reasons related to sequence characteristics or utilization limits). Probe design can be limited by one or more of the follow reasons:

  • Genes which share high sequence homology to each other
  • Genes with repetitive or low complexity sequence content
  • Very short genes which have few candidates for where probes can be placed

For more details on Xenium probe design, please see our support site.

Sensitivity may be lower than expected for these genes or in the worst case, they may not be successfully detected at all. You may wish to remove them to avoid this risk. It may also be possible to design additional probes in an advanced custom design.

How do I interpret this?

This plot shows hierarchical clustering of the single cell data you selected, subset to genes on your panel that are expressed. A higher z-score (calculated across cell types) indicates that the gene is expressed at a higher level in that cell type compared to other cell types in the dataset.

To ensure optimal results, you should make sure that:

  1. Each cell type forms a distinct cluster (a different pattern per row), indicating that you will likely be able to identify the relevant cell types in your Xenium data
  2. Each cell type has an appropriate number of marker genes (the number may vary depending on how deeply you intend to characterize the cell type)
  3. A gene is not expressed uniformly across all cell types (i.e. not the same color in all rows)
  4. Your panel genes are present in each single cell reference dataset you provided

All reference data look good!

Every gene on your panel is present in the reference data.

How do I interpret this?

This section shows a variety of potentially useful diagnostic information for troubleshooting panel performance. Off-targets are predicted by BLASTing the probe sequence against a reference transcriptome. If a codeword is suspected to be impacted by non-specific binding events, the codeword could be deprecated and the data could then be relabeled.